build your own speech recognition software

How to build a simple speech recognition app

“In this 10-year time frame, I believe that we’ll not only be using the keyboard and the mouse to interact but during that time we will have perfected speech recognition and speech output well enough that those will become a standard part of the interface.” — Bill Gates, 1 October 1997

Technology has come a long way, and with each new advancement, the human race becomes more attached to it and longs for these new cool features across all devices.

With the advent of Siri, Alexa, and Google Assistant, users of technology have yearned for speech recognition in their everyday use of the internet. In this post, I’ll be covering how to integrate native speech recognition and speech synthesis in the browser using the JavaScript WebSpeech API .

According to the Mozilla web docs:

The Web Speech API enables you to incorporate voice data into web apps. The Web Speech API has two parts: SpeechSynthesis (Text-to-Speech), and SpeechRecognition (Asynchronous Speech Recognition.)

Requirements we will need to build our application

For this simple speech recognition app, we’ll be working with just three files which will all reside in the same directory:

• index.html containing the HTML for the app.
• style.css containing the CSS styles.
• index.js containing the JavaScript code.

Also, we need to have a few things in place. They are as follows:

• Basic knowledge of JavaScript.
• A web server for running the app. The Web Server for Chrome will be sufficient for this purpose.

Setting up our speech recognition app

Let’s get started by setting up the HTML and CSS for the app. Below is the HTML markup:

Here is its accompanying CSS style:

Copying the code above should result in something similar to this:

Powering up our speech recognition app with the WebSpeech API

As of the time of writing, the WebSpeech API is only available in Firefox and Chrome. Its speech synthesis interface lives on the browser’s window object as speechSynthesis while its speech recognition interface lives on the browser’s window object as SpeechRecognition in Firefox and as webkitSpeechRecognition in Chrome.

We are going to set the recognition interface to SpeechRecognition regardless of the browser we’re on:

Next we’ll instantiate the speech recognition interface:

In the code above, apart from instantiating speech recognition, we also selected the icon , text-box, and sound elements on the page. We also created a paragraph element which will hold the words we say, and we appended it to the text-box .

Whenever the microphone icon on the page is clicked, we want to play our sound and start the speech recognition service. To achieve this, we add a click event listener to the icon:

In the event listener, after playing the sound, we went ahead and created and called a dictate function. The dictate function starts the speech recognition service by calling the start method on the speech recognition instance.

To return a result for whatever a user says, we need to add a result event to our speech recognition instance. The dictate function will then look like this:

The resulting event returns a SpeechRecognitionEvent which contains a results object. This in turn contains the transcript property holding the recognized speech in text. We save the recognized text in a variable called speechToText and put it in the paragraph element on the page.

If we run the app at this point, click the icon and say something, it should pop up on the page.

Wrapping it up with text to speech

To add text to speech to our app, we’ll make use of the speechSynthesis interface of the WebSpeech API. We’ll start by instantiating it:

Next, we will create a function speak which we will call whenever we want the app to say something:

The speak function takes in a function called the action as a parameter. The function returns a string which is passed to SpeechSynthesisUtterance . SpeechSynthesisUtterance is the WebSpeech API interface that holds the content the speech recognition service should read. The speechSynthesis speak method is then called on its instance and passed the content to read.

To test this out, we need to know when the user is done speaking and says a keyword. Luckily there is a method to check that:

In the code above, we called the isFinal method on our event result which returns true or false depending on if the user is done speaking.

If the user is done speaking, we check if the transcript of what was said contains keywords such as what is the time , and so on. If it does, we call our speak function and pass it one of the three functions getTime , getDate or getTheWeather which all return a string for the browser to read.

Our index.js file should now look like this:

Let’s click the icon and try one of the following phrases:

• What is the time?
• What is today’s date?
• What is the weather in Lagos?

We should get a reply from the app.

In this article, we’ve been able to build a simple speech recognition app. There are a few more cool things we could do, like select a different voice to read to the users, but I’ll leave that for you to do.

If you have questions or feedback, please leave them as a comment below. I can’t wait to see what you build with this. You can hit me up on Twitter @developia_ .

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How to Create a Speech Recognition System

Many science fiction writers predicted the creation of virtual voice assistants. And with the development of machine learning and artificial intelligence, speech recognition systems have become a reliable assistant in everyday life.

In modern voice recognition applications, there are serious engineering and natural language processing( NLP) algorithms. Despite the relative complexity of development, the market for virtual assistants continues to grow . So, today we will talk about all aspects of how to make a speech recognition program .

What is a Speech Recognition System?

A voice recognition system is software that "listens" to speech, transforms it into text understandable by a computer, and then manipulates the received data. Almost all modern mobile operating systems have their own voice and speech recognition software to help the user and provide information.

Siri is one of the most advanced speech recognition software. It can give advice by users’ request and execute commands under their guidance. In general, s peech recognition programming greatly simplifies our lives. It allows us to receive important information when we are in a hurry and don’t have time to search the Internet or not be distracted from the road while driving. It is a significant time-saver since the average person speaks 125-150 words per minute while typing only 40 words. In 2019, the U.S. voice recognition technology market was $11 billion, and it is expected to grow by 17% by 2025 . 

History of Speech Recognition Technology

It may seem surprising, but the first experiment to create a machine that recognizes speech dates back to 1,000 AD. A scie ntist and church leader na med Pope Sylvester II developed an instrument that could answer "yes" or "no" questions. Sylvester II used Arab scientific advances to create his devices. Although it was not speech recognition technology, it still used the foundation on which modern software is built: using natural language as input to trigger an action.

Now the speech recognition systems are widely used in many devices and are also part of smart home systems. Even though the first attempts to create similar tools were adopted centuries ago, these technologies have been actively developing over the past 70 years.

Speech Recognition Through the Decades

In 1952, three scientists from Bell Labs developed a device called "Audrey,” which recognized prime numbers from 1 to 9 spoken with one voice.

10 years later, IBM presented a speech recognition system that could "understand" 16 words, including numbers. The system could recognize simple sentences and print the answer out on paper.

A breakthrough happened in the 1970s. The Defense Advanced Research Projects Agency (DARPA) invested in a program called Speech Understanding Research for 5 years. As a result, they created a device called Harpy that "understood" 1011 words. For those times, it was a real win.

In 1970-1980, the Hidden Markov Model was first used for speech recognition. The essence of the model is an emulation of the operation of a random process with unknown parameters. The task of the model is to guess unknown parameters based on the observables. Later, this statistical model was widely used to simulate problems with sequential information and formed the basis of many speech recognition software.

In 1997, the Nuance company launched Dragon Naturally Speaking, which recognized human speech and transformed it into text. The application had hints. It analyzed the words of the dictator, and in a pop-up window, showed unspoken words that fit the structure and meaning of the sentence.

The voice recognition revolution was made by Siri, which was released in 2010 by Siri Inc. After 4 months, Apple bought the software, which eventually became an integral part of devices controlled by iOS. Siri responds to requests, provides recommendations, and interacts with each user individually, analyzing all their requests and behavior.

In 2011, Google released a Voice Search application that aimed at helping users to surf the Internet using voice commands . The application was compatible with Google Chrome. It allowed users to make a voice request, then looked for answers online.

How Speech Recognition Works 

Surrounded by smart gadgets, it seems to us that speech recognition is commonplace. But in reality, it is still a complicated process, even in the information technology age. Just as a small child listens to parents and learns to understand them, speech recognition algorithms have also developed. We taught machines to "understand" what we want from them and send us the needed information. But this process does not stand still, and we continue to teach computers to recognize more and better. Despite that it's possible to write a whole book on this topic, we will try to briefly and easily explain how such programs work.

Simple Word Pattern Matching

This is the easiest way to convert sound to text, which is later processed by the machine. It involves recognizing whole words based on their sound signature. Such systems are often used in answering machines, such as when you call a service center, and the system asks you to pronounce your name or number. The first thing the program does is convert the signal into a form that the machine can understand. Basically, a spectrogram is used for this. This graph has a Y-axis showing frequency, an X-axis showing time and intensity represented in color. 

Spectrogram representing the spectrum of speech frequencies

A spectrogram represents every word in the “memory” of the software. It compares the spectrogram of the word spoken with the spectrograms from its vocabulary to determine what was said. In general, this method does an excellent job of recognizing simple words.

Pattern And Feature Analysis

The disadvantage of the previous model is that it has a limited vocabulary. Theoretically, it could be significantly expanded because people’s vocabularies are very different, and many also have dialects. In turn, it complicates the analysis process for selecting patterns. So, learning blocks that recognize sounds have been invented. It helps the system to understand whole sentences. This is precisely the basis of feature analysis.

Statistical Analysis And Modeling

Some more advanced voice recognition systems are based on the language model. They can listen and understand the words that people say because they have mathematical algorithms for analyzing languages. This method is also built on the rule that a different set of words can follow certain words, while other words are rarely used in the same sentence. For example, it is more likely that the word "open" will be followed by the word "door.” The statistical analysis and modeling method has been actively used over the past 10 years and has reached its development limit. And this means that for better voice recognition programming , more advanced technologies are required.

Artificial Neural Networks (ANNS)

This is how a simple neural network works

Modern systems for speech, text, and photo recognition use neural networks. This is a mathematical model, and its hardware and software implementation allows the computer to work like a human brain. Instead of storing specific patterns, it uses vast networks of neurons that change connections with each other as new information flows through them. But there are also some difficulties here. In order for the neural system to be able to work and develop independently, it will have to be trained using extensive databases.

At KeyUA, we have been creating quality software with machine learning and artificial neural networks technologies for over 5 years.

Popular Voice Assistants

Let's now see what products have succeeded in human speech recognition. And we will start with the one we have already talked about, which made a breakthrough in speech recognition.

Apple’s Siri

Siri was the first voice assistant to be successfully hit the market and integrated into Apple devices. Siri is a development of the International Center for Artificial Intelligence (SRI), an offshoot of a DARPA-funded project described as arguably the largest artificial intelligence project to date. Siri's speech recognition engine was developed by Nuance Communication, but neither Nuance nor Apple admitted this for a long time. Apple’s voice assistant uses sophisticated machine learning techniques to efficiently process queries in real-time , convolutional neural networks, and long-term vs. short-term memory.

It is now used as the primary user interface in the Apple CarPlay infotainment system for cars and integrated with all Apple devices. The user can say "Siri, take me to the airport" or "Siri, I need a car to the train station," and the software will open the travel booking applications installed on the phone. Being the first is never easy, and Siri has received a flurry of criticism from many customers. Nevertheless, its voice recognition libraries continue to be updated, resulting in the program more accurately "understanding" the user. Apple’s voice assistant is available in more than 40 countries in over 20 languages ​​and some dialects. 

Amazon Alexa

This virtual assistant first appeared on Amazon Echo and Amazon Echo Dot smart speakers in 2014. The assistant supports voice communication, playing music, podcasts and audiobooks, making to-do lists, setting alarms, providing up-to-date information about weather, traffic, sports, news, etc., and controlling devices in a smart home. Users can empower Alexa by installing "skills" developed by third-party vendors.

Unlike Apple, Amazon does not impose restrictions on its software’s ability to perform only a certain range of tasks, allowing Alexa to be one of the top speech recognition applications. In addition, it adapts to the user's voice, which helps Alexa understand better over time, even if they speak a dialect.

Microsoft’s Cortana

Cortana was released in 2014 as part of the Windows Phone 8.1 software and was a 26th century AI character from the Halo video game series. After 3 years, Microsoft announced that Cortana's speech recognition rate had reached an accuracy of 95%.

Cortana is designed to anticipate user needs. If desired, she can be given access to personal data such as e-mail, address book, history of searches on the web, etc. - all this data she will use to predict the user’s needs. Also, the virtual assistant is not devoid of a sense of humor: she can maintain a conversation, sing, and tell anecdotes. She will remind you in advance of a scheduled meeting, a friend's birthday, and other important events. The interface has flexible privacy settings that allow users to determine what kind of information to provide to the virtual assistant. Cortana has an age limit - users whose Microsoft accounts are below 13 years old will not be able to use the virtual assistant's services.

Google Assistant

It is a cloud-based personal assistant service that was released in 2016. Despite the fact that the software was launched on the market 2 years after the release of Alexa, it quickly won the love of users, and a year later, it was considered a significant competitor for the Amazon speech recognition system. Google Assistant not only answers correctly but also provides additional context and links to the original website for information. It is available in over 90 countries and 30 languages. Google claims that over 500 million people use their Virtual Assistant every month.

Nuance’s Dragon Assistant and Dragon Naturally Speaking

DragonDictate  (prototype of Dragon NaturallySpeaking) was first released for DOS and used Hidden Markov Models, a probabilistic method for recognizing temporary patterns. At the time, the hardware was not powerful enough to solve the word segmentation problem, and DragonDictate was unable to detect word boundaries during continuous speech input. Users were forced to speak one word at a time, clearly separated by a small pause after each word.

Nowadays, Dragon Naturally Speaking is the basis for speech recognition among many popular products (for example, Siri). It uses a minimal interface and has three main functionalities: voice recognition during dictation with speech-to-written text, voice command recognition, and text-to-speech, reading out the text content of a document.

How to Create Voice Recognition Software

There are 9 basic steps to deliver a speech recognition project . This process takes time, and in general, you will need at least 12 weeks to prepare the product.

How to Make a Speech Recognition Software

#1 Market Analysis

Voice recognition systems are popular throughout the world. They are used in a variety of applications, from defense to children's toys. In 2015, the Hello Barbie doll was released, which had artificial intelligence and was designed to recognize children's speech. It supports more than 8000 lines of dialogues . This widespread use of technology suggests that market analysis is essential no matter the size of your project. If you plan to make a voice recognition program for commercial needs, you should carefully study the niche, competitors in your field, preferences, and behaviors of potential customers. Software becomes successful when it solves certain users' issues. Analysis of potential customers and their characteristics allows you to determine which functionality will be most in demand.

If you create software for your own needs (for example, only your employees will use it), you will still have to do a market analysis to understand what existing tools are better to use while saving time and development costs.

#2 Business Plan Preparation

The business plan analyzes all aspects of the project and ways of product distribution. This document is essential, even if you think you are building a simple application. First, a business plan allows you to think about solutions in advance for all sorts of problems a company may face. Secondly, by having this document, you can effectively control the consumption of resources for software development. Finally, this is an excellent opportunity to attract investors if you need additional funding.

#3 Creating a Specification

Your next step is documenting how the finished product should work. In the specification, you have to describe how the system will perceive sounds (through a microphone, audio files, etc.), how users will interact with the application, and its additional features. All this will later become the basis for the development of the code.

#4 Selecting Contractors

This is an essential step since the quality of your product will depend on the experience and knowledge of the developers. You can hire freelancers or an IT outsourcing company. Usually, the services of freelancers are cheaper, but this also has its drawbacks. If your project requires more than one developer, you will have to search and hire each specialist yourself. This is a time-consuming process. And if you have never done it before, then you risk hiring non-professional personnel. IT companies provide dedicated teams. This means that your project will be worked on by as many specialists as you need, including developers, designers, testers, and marketers.

When choosing a contractor, also pay attention to what technologies they use. At the moment, Python, which is an open-source programming language, is especially popular when creating a voice recognition system . In Python, developers can use various speech identification services through the API.

#5 Preparing Designs

Take care of preparing the page layouts for your application. If the designer is someone from the contractor's company, it will be a significant advantage. Together with the developers, they can think over in detail a first-class and convenient interface for your product.

#6 Code Development

The next step is the longest and includes building the project architecture and generating the source code. Development can take three months or more, depending on the scale of the required product. This process is based on transforming the specification into workable software, configuring the server , and installing the database. Building a speech recognition system implies writing algorithms for neural systems and machine learning . These are complex technologies that only professional programmers can deliver in a quality manner. 

Before a product reaches the end-user, it must be thoroughly tested. This is the responsibility of the quality assurance team. Unfortunately, many companies skip this step, trying to save money, ultimately leading to even higher costs or project failure. Testing is about checking the product’s actual functionality and comparing it with the specification requirements and business logic. This step allows you to identify all the flaws in the system and voice recognition methods before your customers try the product. Testing is a guarantee that the market will receive high-quality software.

#8 Marketing Creation

In parallel with testing your product, you should start developing strategies for promoting your product to the market. If you are creating software for the internal needs of your company, this step can be skipped. In all other situations, you need a strong marketing campaign to launch a product successfully.

#9 Launch and Maintain

Once development and testing are complete, it's time to market your product and launch promotion campaigns. If you have created a mobile application for Android and/or iOS, it should be published in the appropriate Stores. But app development doesn't end there. A truly successful product involves maintaining the functionality and periodically updating the tools. Since voice recognition technologies are constantly being updated, your product will also require changes to meet market needs and IT trends.

Now speech recognition systems are widely used in many devices and are also part of smart home systems. Although the first attempts to create similar tools were adopted centuries ago, these technologies have been actively developing over the past 70 years.

Voice typing is an efficient form of computing that makes our daily tasks easier. Modern, powerful systems are built based on several programming languages: Python, Java , C ++, Objective-C. Their libraries and engines are continually being updated, making speech recognition systems more accurate. Many studies predict that the role of virtual assistants in our lives will become more significant. So building voice recognition software is a great business idea.

Have a great idea, and want to make custom voice recognition software? Great, let's put your concept into practice at the highest level!

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How to Make a Speech Recognition System

By Sam Palmer

1 year of experience

Sam is a professional writer with a focus on software and project management. He has been writing on software-related topics and building PHP based websites for the past 12 years.

Before we jump into how to make a speech recognition system, let’s take a look at some of the tools you can use to build your own speech recognition system.

Commercial APIs

Many of the big cloud providers have APIs you can use for voice recognition. All you need to do is query the API with audio in your code, and it will return the text. Some of the main ones include:

• Google Speech API
• Microsoft Cognitive Services – Bing Speech API
• Amazon Alexa Voice Service
• Facebook’s Wit.ai

This is an easy and powerful method, as you’ll essentially have access to all the resources and speech recognition algorithms of these big companies.

Of course, the downside is that most of them aren’t free. And, you can’t customize them very much, as all the processing is done on a remote server. For a free, custom voice recognition system, you’ll need to use a different set of tools.

Open Source Voice Recognition Libraries

To build your custom solution that recognizes audio and voice signals, there are some really great libraries you can use. They are fast, accurate, and free. Here are some of the best available – I’ve chosen a few that use different techniques and programming languages.

CMU Sphinx is a group of recognition systems developed at Carnegie Mellon University – each designed for different purposes. It is written in Java, but there are bindings available for many languages. This means you can use the libraries and voice recognition methods even if you want to program in C# or Python. There are some great components you need to  develop a voice recognition system.

For an awesome example of an application built using CMU Sphinx, check out the Jasper Project  on GitHub.

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Kaldi , released in 2011 is a relatively new toolkit that’s gained a reputation for being easy to use. It uses the C++ programming language.

HTK, also called the Hidden Markov Model Toolkit, is made for the statistical analysis modeling techniques. It’s owned by Microsoft, but they are happy for you to use and change the source code. It uses the C programming language.

Where to Get Started?

If you’re new to building this kind of system, I would suggest you to go with something based on Python that uses the CMU Sphinx library. Check out this quick tutorial that sets up a very basic system in just 29 lines of Python code.

Finding Developers That Can Help

Needless to say, speech recognition programming is an art form, and putting all this together is a heck of a job. To create something that really works, you’ll need to be a pro yourself or get some professional help. Learn how to build an agile development team and why it’s important for the success of your app.

Software teams at DevTeamSpace build these kinds of systems all the time and can certainly help you get your app to understand your users very quickly.

Key considerations while implementing the speech recognition technology

Keep the following key questions and considerations in mind when you create and implement speech recognition software:

1. Define your business problems or opportunities to find the right use case

By now, you know that building a speech recognition system involves complexities. You need to first analyze your business problems and opportunities. Assess whether you have a viable use case for using the speech recognition technology.

Speech recognition technology has given rise to applications facilitating voice search and recognizing speech signal. Digital assistants like Apple’s Siri accept voice commands from users and respond to their requests.

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Many sectors like healthcare, government, etc. have high-value use cases involving this promising technology, and your organization might have one too. Identify the right use case.

2. Decide the functionality and features to offer

A user of an Apple iPhone has certain specific needs when using Apple’s Siri. Similarly, Google Home and other popular automatic speech recognition software deliver tangible value to users. These organizations undertook large scale studies to determine the scope of their “Artificial Intelligence” (AI) projects.

They often pushed the boundary and offered very helpful features. E.g., “Apple Dictation” is a useful speech-to-text app for Apple devices. Another example is the “Voice Access” app from Google. It helps users to make phone calls in hands-free mode.

You need to study your business requirements carefully. Subsequently, you need to decide the functionality and features to offer. Plan to support all key operating systems.

3. Plan the project meticulously

Plan meticulously so that you prepare sufficiently for the entire AI development lifecycle . Do the following:

• Define why you would use AI and what you will automate.
• Identify relevant data sources and gather large enough datasets consisting of various speech patterns to build a large vocabulary speech recognition solution.
• Determine the AI capabilities you need, e.g., “Deep Learning” (DL), “Natural Language Processing” (NLP), speech recognition, etc.
• Evaluate popular SDLC methodologies like Agile and choose a suitable methodology.
• Plan the relevant phases like requirements analysis, design, development, testing, deployment, and maintenance.

4. Decide the technical capabilities you will use, e.g., “Speech-to-text”

Depending on your business requirements, you need to choose one or more technical capabilities within the large landscape of AI. E.g., you might need to explore the following:

• “Machine Learning” (ML);
• “Deep Learning” (DL);
• Acoustic modeling for speech recognition;
• Generating optimal word sequences using “Automatic Speech Recognition” (ASR) systems;
• Using acoustic modeling for recognizing phonemes, which could help with speech recognition;
• “ Hidden Markov Model ” (HMM) decomposition, which helps to recognize speeches where there’s interference from another background speaker or background noise;
• Using continuous speech recognition;
• “Limited vocabulary” speech recognition techniques;
• Measuring speech recognition accuracy by using the “Word Error Rate” (WER);

5. Developing capabilities vs using 3 rd party APIs

You will likely design and develop software to suit your requirements. For this, you will likely code algorithms and modules using Python . There are very good tutorials to create speech recognition software using Python, which will help.

In some scenarios, you might want to use market-leading APIs. This could save some time since you won’t reinvent the wheel. The following are a few examples of such APIs:

• The “ Speech-to-text ” API from Google Cloud: This API helps you to transcribe your speech data in real-time;
• The Automatic Speech Recognition (ASR) system from Nuance: Nuance offers an ASR system, which is especially helpful for customer self-service applications;
• IBM Watson “Speech to text” API: You can use to add capabilities to transcribe speech signals;
• “Speech Recognizers” like CMU Sphinx “Recognizer”.

Planning to Implement a Speech Recognition System?

Speech recognition tech is finally good enough to be useful. Pair that with the rise of mobile devices (and their annoyingly small keyboards), and it’s easy to see it taking off in a big way. To keep up with your competition and make your customers happy, why not learn how to make a voice recognition program and implement it into your products?

If you are looking for experienced software engineers to help you with the development of a speech recognition solution, DevTeam.Space can help you.

Get in touch via this quick form stating your initial requirements for speech recognition system project. One of our technical managers will get back to you and connect you with field-expert software developers experienced in developing market-competitive speech recognition platforms.

Frequently Asked Questions

It is a software system that is able to recognize what people are saying to it. Speech recognition systems vary from simple human speech recognition saying yes or no to sophisticated machine learning programs such as SIRI understanding spoken language using complex neural networks. 

The process is simple. As the machine listens to the human voice it breaks down the sounds in such a way that it is able to recognize individual words. More sophisticated programs use machine learning to improve the accuracy of a speech recognition task. Such systems are able to learn accents, different pitches, tones of voice, etc.  

Any program that requires machine learning will require a team of expert developers including voice recognition software. If you have such developers then they will be able to build a voice recognition technology for you. If you don’t, however, then you should onboard developers from an experienced software development platform such as DevTeam.Space to build next-level speech recognition applications.

Alexey Semeney

Founder of DevTeam.Space

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Alexey is the founder of DevTeam.Space. He is award nominee among TOP 26 mentors of FI's 'Global Startup Mentor Awards'.

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Train Your Own Speech Recognition Model in 5 Simple Steps using python

Learn how to build a custom speech recognition model from scratch using python in just 5 easy steps. this step-by-step guide will take you through the entire process, from collecting speech data to training the model and testing its accuracy..

“ Artificial Intelligence is the new Electricity “— Andrew Ng

Machine Learning is an exciting branch of computer science that enables solutions to a lot of problems, one of the gems of it is speech recognition. How fascinating it is when you say to your phone for playing some music and it does so just by listening to your voice. Ever wondered how it is able to detect and recognize your voice so accurately? The answer is simple it runs some kind of neural network inside which is trained on a vast amount of data. All the speech recognition services which we use in our daily life like Google assistant, Apple’s Siri, Amazon’s Alexa are owned by some tech giants which have a colossal amount of data to train their speech recognition model. To use the services provided by these tech giants we need to pay them for every hit we make to their services. So if you want to make your own speech recognition service and you have enough data why go with these services you can train your own model. Luckily there is one open-source model available which is based on Baidu’s Deep Speech research paper and referred to as Mozilla DeepSpeech. You can train your own DeepSpeech model in five simple steps which I will explain but before that, there are some perquisites for training.

Prerequisites of Mozilla DeepSpeech :

• Git Large File Storage
• Mac or Linux environment
• GPU, CUDNN, and CUDA enabled system

Step 1: Preparing Data

Assuming you have a large amount of data for training the DeepSpeech model in audio and text files, you need to reform the data in a CSV file that is compatible with training it. The desired format of data for training the DeepSpeech model is:

You need to have all your filenames and transcript in this manner. For getting the file-size you can use the following code-

you need to create three CSV files naming train.csv, dev.csv, and test.csv for training, validation, and testing respectively.

Step 2: Cloning the Repository and Setting Up the Environment

Once your data is prepared we can start with installing dependencies and setting up the training environment. Start by cloning the DeepSpeech repository.

Once you have cloned the repository it’s time to set up the environment for training.

By running these commands a virtual environment for training the DeepSpeech model will be created.

Step 3: Installing Dependencies for Training

Once you have activated the training environment you need to install some dependencies for training your DeepSpeech model. Traverse to the DeepSpeech folder which you have cloned in step 2 and run the following:

this will install all the requirements for training.

Step 4: Downloading Checkpoint and Creating Folder for Storing Checkpoints and Inference Model

if you want to fine-tune the DeepSpeech model on your data you can download the deep speech 0.6.0 checkpoint from here : https://github.com/mozilla/DeepSpeech/releases/download/v0.6.0/deepspeech-0.6.0-checkpoint.tar.gz once you have downloaded the checkpoint, create two folders as follows:

Now paste the downloaded checkpoints into the fine_tuning_checkpoints folder.

If you want to train a model from scratch there is no need to download the checkpoint but you still need to create those two folders. You have done all the setup for training, the next step is to start training the model.

Step 5: Training DeepSpeech model

If you have completed all the previous steps successfully now is the time for training the DeepSpeech model on your own data. For that you just need to run the following command :

Once you run the mentioned command the training will start. When the training completes the inference model will be stored in the output_models folder with the name output_graph.PB you can use it to test on other data.

Follow all these steps and you will be able to train your own DeepSpeech model easily.

References:

[1] Source Code — https://github.com/mozilla/DeepSpeech

[2] Research Paper — https://arxiv.org/abs/1412.5567

[3] https://deepspeech.readthedocs.io/en/v0.7.4/TRAINING.html

Thanks for reading, See you next time !!!❤️

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How to Build an Effective Speech Recognition System

Written by:

AI Team Leader

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Speech recognition targets at translating individual speech into a text format, to further process words and extract meaningful information from it. This can be used in various scenarios where UI is implemented with voice control features, or reacting to certain words with assigned actions is important. In this post, we’ll focus on the general approach for speech recognition applications, and elaborate on some of the architectural principles we can apply to cover all of the possible functional requirements.

MobiDev has been working with artificial intelligence models designed for processing audio data since 2019. We’ve built numerous applications that use speech and voice recognition for verification, authentication and other functions that brought value to our client’s. If you’re looking for tech experts to support your speech recognition project of any complexity, consider getting in touch with MobiDev experts.

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How Do Speech Recognition Applications Work?

Speech recognition covers the large sphere of business applications ranging from voice-driven user interfaces to virtual assistants like Alexa or Siri. Any speech recognition solution is based on the Automatic Speech Recognition (ASR) technology that extracts words and grammatical constructions from the audio, to process it and provide some type of system response. 

Voice Recognition vs Speech Recognition

Speech recognition is mostly responsible for extracting meaningful data from the audio, recognizing words and the context they are placed in. This should not be mistaken with Voice Recognition, which targets human voice timbres to identify the owner’s voice from other surrounding sounds or voices. 

Voice recognition is used in biometric authentication software that uses user’s biometric data such as voice, iris, fingerprint, or even gait to verify the persona and provide access. The pipeline for voice and speech recognition may be different in terms of data processing steps, so these technologies should not be mistaken. Though, they are often used in conjunction. 

If you are interested in biometric authentication software, you can read our dedicated article where we describe our practical experience implementing office security systems based on voice authentication and face recognition.

Which type of AI is used in speech recognition?

Speech recognition models can react to speech directly as an activation signal for any type of action. But since we’re speaking about speech recognition, it is important to note that AI doesn’t extract meaningful information right from the audio, because there are many odd sounds in it. This is where speech-to-text conversion is done as an obligatory component to further apply Natural Language Processing or NLP. 

So the top-level scope of a speech recognition application can be represented as follows: the user’s speech provides input to the AI ​​algorithm, which helps to find the appropriate answer for the user.

High-level representation of an automatic speech recognition application

However, it is important to note that the model that converts speech to text for further processing is the most obvious component of the entire AI app development pipeline. Besides the conversion model, there will be numerous components that ensure proper system performance.

So approaching the speech recognition system development, first you must decide on the scope of the desired application:

• What will the application do?
• Who will be the end users?
• What environmental conditions will it be used in?
• What are the features of the domain area?
• How will it scale in the future?

What is important for speech recognition technology?

When starting speech recognition system development, there are a number of basic audio properties we need to consider from the start: 

• Audio file format (mp3, wav, flac etc.)
• Number of channels (stereo or mono)
• Sample rate value (8kHz, 16kHz, etc.)
• Bitrate (32 kbit/s, 128 kbit/s, etc.)
• Duration of the audio clips.

The most important ones are audio file format and sample rate, so let’s speak of them in detail. Input devices record audio in different file formats, and most often audio is saved in loosy mp3, but there can also be lossless formats like WAV or Flac. Whenever we record a sound wave, we basically digitize the sound by sampling it in discrete intervals. This is what’s called a sample rate, where each sample is an amplitude of a waveform in a particular duration of time.

Audio signal representation

Some models are tolerant to format changes and sample rate variety, while others can intake only a fixed number of formats. In order to minimize this kind of inconsistency, we can use various built-in methods for working with audio in each programming language. For example, if we are talking about the Python language, then various operations such as reading, transforming, and recording audio can be performed using the libraries like Librosa , scipy.io.wavfile and others.

Once we get the specifics of audio processing, this will bring us to a more solid understanding of what data we’ll need, and how much effort it will take to process it. At this stage, consultancy services from a data science team experienced in ASR and NLP is highly recommended. Since gathering wrong data and or setting unrealistic objectives are the biggest risks in the beginning.

Automatic Speech Recognition process and components

Automatic speech recognition, speech-to-text, and NLP are some of the most obvious modules in the whole voice-based pipeline. But they cover a very basic range of requirements. So now let’s look at the common requirements to speech recognition, to understand what else we might include in our pipeline:

• The application has to work in background mode, so it has to separate the user’s speech from other sounds. For this feature, we’ll need voice activity detection methods, which will transfer only those frames that contain the target voice.
• The application is meant to be used in crowded places, which means there will be other voices and surrounding noise. Background noise suppression models are preferable here, especially neural networks which can remove both low-frequency noise, and high frequency loud sounds like human voices.
• In cases where there will be several people talking, like in the case of a call center, we also want to apply speaker diarization methods to divide the input voice stream into several speakers, finding the required one.
• The application must display the result of voice recognition to the user. Then it should take into account that speech2text (ASR) models may return text without punctuation marks, or with grammatical mistakes. In this case, it is advisable to apply spelling correction models, which will minimize the likelihood that the user will see a solid text in front of them.
• The application will be used in a domain area, where professional terms and abbreviations are used. In such cases, there is a risk that speech2text models will not be able to correctly cope with this task and then training of a custom speech2text model will be required. 

In this way, we can derive the following pipeline design which will include multiple modules just to fetch the correct data and process it.

Automatic Speech Recognition (ASR) pipeline

Throughout the AI pipeline, there are blocks that are used by default: ASR and NLP methods (for example, intent classification models). Essentially, the AI algorithm takes sound as an input, converts it to speech using ASR models, and chooses a response for the user using a pre-trained NLP model. However, for a qualitative result, such stages as pre-processing and post-processing are necessary. 

Basic implementation described here will be helpful if you already have a team with relevant experience and engineering skills. If you need to fill the expertise gap in specific areas of audio processing, MobiDev offers engineers or a dedicated team to support your project development. But if you don’t have clear technical requirements and struggle to come up with the strategy, let’s move on to advanced architecture where we can provide consulting, including full audit of the existing infrastructure, documenting the development strategy and running you through technical supervision if needed.

Our 4 recommendations for improving quality of ASR 

To optimize the planning of the development and mitigate the risks before you get into trouble, it is better to know of the existing problems within the standard approaches in advance. MobiDev ran an explicit test of the standard pipeline, so in this section will share some of the insights found that need to be considered.

1. Pay attention to the sample rate

As we’ve mentioned before, audio has characteristics such as sample rate, number of channels, etc. These can significantly affect the result of voice recognition and overall operation of the ASR model. In order to get the best possible results, we should consider that most of the pre-trained models were trained on datasets with 16Hz samples and only one channel, or in other words, mono audio. 

This brings with it some constraints on what data we can take for processing, and adds requirements to the data preparation stages.

2. Normalize recording volume

Obviously, ASR methods are sensitive to audio containing a lot of extraneous noise, and suffer when trying to recognize atypical accents. But what’s more important, speech recognition results will strongly depend on the sound volume.  Sound recordings can often be inconsistent in volume due to the distance from the microphone, noise suppression effects, and natural volume fluctuations in speech. In order to avoid such inaccuracies, we can use the Pyloudnorm library from the Python language that helps to determine the sound volume range and amplify the sound without any distortion. This method is very similar to audio compression, but brings less artifacts, improving the overall quality of the model’s predictions.

Nvidia Quarznet 15×5 speech recognition results with and without volume normalization

Here you can see an example of voice recognition without volume normalization, and also with it. In the first case, the model struggled to recognize a simple word, but after volume was restored, the results improved.

3. Improve recognition of short words

The majority of ASR models were trained on datasets that contain texts with proper semantic relations between each sentence. This brings us to another problem with recognizing short phrases taken out of context. Below is a comparison of the performance of the ASR model on short words taken out of context and on a full sentence:

The result of recognizing short words in and out of context

In order to overcome this problem, it is necessary to think about the use of any preprocessing methods that allow the model to understand in which particular area a person wants to receive information more accurately.

Additionally, ASR models can generate non-existing words and other specific mistakes during the text to speech conversion. Spell correction methods may simply fail in the best cases, or choose to correct the word to one that is close to the right choice, or even change to a completely wrong one. This problem also applies to very short words taken out of context, but it should be foreseen in advance. 

4. Use noise suppression methods only when needed

Background noise suppression methods can greatly help to separate a user’s speech from the surrounding sounds. However, once loud noise is present, noise suppression can lead to another problem, such as incorrect operation of the ASR model. 

Human speech tends to change in volume depending on the part of the sentence. For example, when we speak we would naturally lower our voice at the end of the sentence, which leads to the voice blending with other sounds and being drowned out by the noise suppression. This results in the ASR model not being able to recognize a part of the message. Below you can see an example of noise suppression affecting only a part of a user’s speech.

Noise suppression effect on the speech recognition

It is also worth considering that as a result of applying Background Noise Suppression models, the original voice is distorted, which adversely affects the operation of the ASR model. Therefore, you should not apply Background Noise Suppression without a specific need for it. In our short demo, we demonstrate how ASR model handles general voice processing, as well as with the application of noise suppression, so you can check how it looks like in real life.

How to Move On With ASR Development? 

Based on the mentioned points, the initial pipeline can bring more trouble with it than actual performance benefits. This is because some of the components that seem logical and obligatory may interrupt the work of other essential components. In other cases, there is a strict need to add layers of preprocessing before the actual AI model can interact with data. We can therefore come up with the following enhanced ASR system architecture:

Enhanced automatic speech recognition system pipeline

That is why, based on the above points, noise suppression and spelling correction modules were removed. Instead, to solve the problem of removing noise and getting rid of errors in the recognized text, the ASR model has to be fine-tuned on the real data. This data will fully reflect the actual environmental conditions and features of the domain area.

While audio processing AI modules may seem easy to implement compared to computer vision tasks, and the amount of data required, there many aspects you need to learn about before hiring engineers. If you have troubles at the initial stages and won’t to move on without a clear development strategy, you can contact us to get concise answers to your questions, receive information on the preliminary budget and development timelines. MobiDev stands for transparent communication, so we product a list of artifacts such as technical vision and development strategy that includes POC stage to clarify the requirements with the client. Feel free to leave your contact through the contact form down below if you need AI consultancy for building robust speech recognition software.

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How to Build a Basic Speech Recognition Network with Tensorflow (Demo Video Included)

Introduction

This tutorial will show you how to build a basic speech recognition network that recognizes simple speech commands. Speech recognition is a subfield of computer science and linguistics that identifies spoken words and converts them into text.

A Basic Understanding of the Techniques Involved

When speech is recorded using a voice recording device like a microphone, it converts physical sound to electrical energy. Then, using an analog-to-digital converter, this is converted to digital data, which can be fed to a neural network or hidden Markov model to convert them to text.

We are going to train such a neural network here, which, after training, will be able to recognize small speech commands.

Speech recognition is also known as:

• Automatic Speech Recognition (ASR)
• Computer Speech Recognition
• Speech to Text (STT)

The steps involved are:

• Import required libraries
• Download dataset
• Data Exploration and Visualization
• Preprocessing

Here is a colab notebook with all of the codes if you want to follow along.

Let us start the implementation.

Step 1: Import Necessary Modules and Dependencies

Step 2: download the dataset.

Download and extract the  mini_speech_commands.zip , file containing the smaller Speech Commands datasets.

The dataset's audio clips are stored in eight folders corresponding to each speech command:  no , yes , down , go , left , up , right ,and stop.

Dataset: TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words by thousands of different people. We will be working with a smaller version of the Speech Commands dataset called mini speech command datasets.

Download the mini Speech Commands dataset and unzip it.

The dataset's audio clips are stored in eight folders corresponding to each speech command:  no   yes down   go   left   up   right and  stop

Now that we have the dataset, let us understand and visualize it.

Step 3: Data Exploration and Visualization

Data Exploration and Visualization is an approach that helps us understand what's in a dataset and the characteristics of the dataset. Let us visualize the audio signal in the time series domain.

Here is what the audio looks in a waveform.

To listen to the above command up :

Check the list of commands for which we will be training our speech recognition model. These audio clips are stored in eight folders corresponding to each speech command:  no ,  yes ,  down ,  go ,  left ,  up ,  right , and  stop.

Remove unnecessary files:

Let us plot a bar graph to understand the number of recordings for each of the eight voice commands:

As we can see, we have almost the same number of recordings for each command.

Step 4: Preprocessing

Let us define these preprocessing steps in the code snippet below:

Convert the output labels to integer encoded labels and then to a one-hot vector since it is a multi-classification problem. Then reshape the 2D array to 3D since the input to the conv1d must be a 3D array:

Step 5: Training

Train test split - train test split is a model validation procedure that allows you to simulate how a model would perform on new/unseen data. We are doing an 80:20 split of data for training and testing.

Create a model and compile it. Now, we define a model:

Define callbacks:

Start the training:

Plot the training loss vs validation loss:

Step 6: Testing and Prediction

Now, we have a trained model. We need to load it and use it to predict our commands.

Load the model for prediction:

Start predicting:

You can always create your own dataset with creative ways like clap sounds and whistles or your own custom words and train your model to recognize them.

Let us now check out the demo video for this experiment.

Parting Thoughts

We just completed a tutorial on building a speech recognition system! Here is a quick recap:

We began by exploring the dataset, giving us a good feel for what is inside. Then, we prepped the data, converting it into a format suitable for training. After a train-test split, we designed a Conv1D neural network for the task.

By following these steps, we have laid the foundation for a speech recognition system. With further tweaks and your own data, you can expand its capabilities. Keep exploring the world of speech recognition!

Table of Contents

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Create A Simple Speech Recognition Application

• ‘Getting Started’ Sample Solution
• IVR with Transfer
• Speech Recognition Sample Solution
• Conferencing
• Amazon Lex Chatbot
• Activate your Cloud Services Production Account
• Welcome to Voice Elements
• System Requirements for Voice Elements Demo Samples
• Start Coding Voice Elements
• Voice Elements Platform
• Voice Elements Platform Benchmarks
• Benefits of Using Voice Elements Platform
• Getting Started with Voice Elements
• Why Should I Develop My Own Voice Application?
• Free Telephony Software
• Voice Elements Skeleton Project – C
• Class Diagram
• Logging in Voice Elements
• What Are the Deployment Options for Voice Elements?
• What Are the Different Connection Mode Options?
• Licensing Questions
• License Agreement
• Introduction to Free Application Hosting
• How To Build a Voice App in Under 10 Minutes (video)
• Common Voice Elements Terms Explained
• Common SIP Terms Explained
• Add Additional Voice Resources
• Add Service Dependencies
• Build a T1 Crossover Cable
• Compile for a 64 bit OS (for legacy versions prior to March 2010)
• Connect to my SIP Carrier
• Connect to the Inventive Labs Telephony Bank Service
• Create a Conference Application Using C#
• Create a New VE Server from an Existing VE Installation
• Create a Simple IVR
• Have a Voice Elements Application Accept all Calls Except One Number
• Perform Music on Hold
• Play a File
• Use Play and Record
• Record the Entire Length of a Call
• RegisterDNIS()
• Run Signed Versions of Voice Elements or HMP Elements
• Set Up and Test Your SIP Registration
• Set Up Global Tones with Sample Code
• Setup QoS Settings in Windows
• Use Multiple SIP Carriers
• Use The Beep Detector
• Write Outbound Dialer Applications in C#
• Why Am I Missing Digits
• Why Do I Get Skipping or Jitter?
• Why Do I Get the Error “Connection Refused”?
• Why Does Voice Elements SIP Platform Time Out When Starting Up?
• Why Doesn’t My Voice File Sound Clear?
• What Does This Setting Do?
• What is the Preferred Way to Handle Hangup Events?
• How Many Concurrent Ports Can I Run?
• What is a Code Example of ‘GetAllChannels()’?
• What Hardware Do You Recommend For Voice Elements on a VM?
• What Kind of Router Do You Recommend To Handle VoIP Traffic?
• What’s Needed To Set Up a Click To Talk Account
• Why Am I Getting the Wrong Call Progress Results Back?
• Why Doesn’t HMP Elements Receive the Call That I Send It?
• Why Does Call Progress Unexpectedly Return An Operator Intercept?
• Where is CTI32?
• Beep Detector Strategies
• Beep Detection Default Settings
• Call Analysis Features
• Conferencing Overview
• Conferencing Settings
• Configuration Settings – HMP Elements
• Configure with AudioCodes Gateway (on TDM Equipment)
• Installation with Voice Elements
• Multiple SIP Carriers on HMP Elements
• Router Configuration
• Running in PCAP mode
• Running Inside of a Virtual Machine
• Secure Calls with SRTP or SIP TLS
• Troubleshoot with Wireshark
• International Dial Tones
• Port Mirroring
• How Come the Call I Place Does Not Work?
• Positive Call Delivery – Destination Groups
• Improving Speech Recognition Results
• QOS Settings for HMP Elements
• Recording Calls Automatically
• SIP Options in HMP Elements
• Specifying embedded XML tags
• Telephony Server Log – No Subscribers Found
• Time a Message to Play to a Voice Mail System
• Border Elements – Our Powerful Session Border Controller (SBC)
• Setup Border Elements
• Border Elements Configuration Website
• Border Elements – Rule Procedure
• WebRTC – Getting Started
• How to Configure WebRTC
• Call Center Phone
• Load Balancing
• Setting up Secure WebSockets
• Creating Your Own Custom WebRTC Form
• Test Voice Elements Platform On Your Own Machine Using WebRTC
• Creating a Self Signed Certificate
• Web-based ScreenPop
• WebRTC Demos
• Configuring Yealink T19/T20 Phones
• Mobile Elements Client
• Mobile Elements Server Configuration
• Placing Outbound Calls with Mobile Elements on iOS Device
• Placing Outbound Calls with Mobile Elements on Android Device
• Placing Outbound Calls with Mobile Elements on UWP Device
• Receiving Inbound Calls with Mobile Elements on Android Device
• Receiving Inbound Calls with Mobile Elements on UWP Device
• Voice Elements Client Toolkit
• Voice Elements Developer Toolkit
• Resource Management
• Setting the SIP Response Code to return when all your lines are busy
• Speech Recognition in Voice Elements
• Install Microsoft Speech Platform
• Using the Microsoft Speech Platform
• Test Speech Recognition with Voice Elements
• Create Microsoft Speech Compatible Grammar Files
• Using Text-To-Speech Voices
• Test Microsoft Speech TTS with Voice Elements
• Resolving Text to Speech Issues
• What TTS Voices Do I Have Available?
• Change from English to Spanish TTS
• Dynamically Switch Between Different TTS Voices or Languages
• Faxing Tips and Tricks
• Install Ghostscript for PDF To TIF functionality
• Voice Elements Class Reference Library
• Firewall Configuration for SIP Trunking
• Server General Application Behavior
• Tips to Run High-Density Voice Elements-based Systems
• Running Voice Elements Platform in a Virtual Machine
• Troubleshooting Crosstalk
• Troubleshooting using DebugView
• Adding Link to Click To Talk Form
• Setting up your Voice Elements Application for Redundancy
• Yealink T19/T20 Phones
• Setting up the Linksys SPA2102 with STUN / REGISTRATION to talk with Border Elements
• Setting up X-Lite with STUN / REGISTRATION / RPORT to talk with Border elements
• Encrypting your Application’s Connection to the Voice Elements Server
• Understanding the Different Voice Resource Properties
• Running Voice Elements Platform on Azure VM
• VapMap Files
• Voice Elements Client (Developer) – Release Notes
• Support Policy
• How To Build A Voice App
• Install Your Application as a Service
• Installing Voice Elements Premise Software
• Using the Voice Elements Dashboard
• Connect to Inventive Labs SIP Trunking
• Connect to a Third Party SIP Carrier
• Connect to your SIP based PBX or Gateway
• Hmp Elements Server Config File
• Voice Elements Server Config File
• Configuring Server General Application Behavior
• Add or Change Usernames and Passwords
• SMS via REST API
• Voice Elements SMS
• Industry Update: 10DLC A2P Surcharges
• Toll-Free Messaging Verification
• 10DLC SMS Campaign Registration
• Messaging Consent and Opt In Guidance
• How to Port In Local Numbers
• How to Port in Toll Free Numbers
• Setting Up Inventive Labs’ SIP Service
• How Do I Keep My Calls From Being Labeled as Spam?
• How To Set Up Your DNS Entry
• Why You Need “Teams Service Admin” Role to set up your Account Users
• What is the Microsoft “Teams Phone Standard” License (aka Phone System License)
• Terms of Use
• Email-Texting Product Description
• Support Program
• Add and Return Phone Numbers
• Manage SMS Campaigns
• Customer FAQs
• CTI32 Version V4.6 Release Notes
• Understanding the CTI32 Service
• Install CTI32 with HMP Elements
• Installing HMP Elements with CTI32
• The built-in CTI32 Database Access Class
• Concurrent CTI32 Engines
• Multiple SIP Registrations with CTI32
• CTIPlayAndRecognizeWord
• CTISetStdCspCallBack
• Continuous Speech Processing (CSP)
• CSP Voice Formats
• NAT Assistant
• Problems Dialing Out Over a PRI
• RTF Logging 101
• Use an Interactive Debugger with CTI32
• Configure LumenVox with CTI32
• Protocol Information For GlobalCall
• Call Progress on Springware and Dialogic DNI Boards
• Low Level and Legacy Telephony Configuration
• Q.931 CauseCodes
• Using the Built-In CTI32 Database Access Class
• Programmable Voice
• Speech Recognition

Traditionally, IVRs used DTMF (or digits entered by a user on their phone) to better direct users. As Speech Recognition technology has improved, more and more companies are taking advantage of this technology to improve their user experience.

Voice Elements Makes Speech Reco Easy

It’s very easy to build Speech Recognition applications using Voice Elements. Voice Elements supports both Lumenvox and the Microsoft Speech Platform   that has support for 18 languages.

Grammar Files

Grammar files contain lists of words that you would like to be able to recognize. For example, if you wanted to let the user identify which state they are calling from, you could create a grammar file with all 50 states.

It’s very easy to create grammar files. For more information , please see our article “ Create Microsoft Speech Compatible Grammar Files .”

Here is a simple grammar file YesNo.gram that allows the user to say “Yes”, “No”, “Correct”, “Incorrect”, “Negative”, or whatever items you define.

Speech Recognition Code Sample

Note a few key items from the code sample above.

SpeechRecognitionPermitBargeIn

You can allow a user to “BargeIn” while you are performing a play. This way a user doesn’t have to wait until the audio is finished. However, this can sometimes negatively affect Speech Recognition performance (for example, if a user is using speakerphone, it may pick up trailing audio from what you are playing back).  VoiceResource.SpeechRecognitionPermitBargeIn Property

Determining What Score to Use

The score that is returned by the Speech Recognition engine ranges from 0-1000. Generally, anything above a 700 could be considered a good positive score. However, you will want to do some testing on your own to determine an appropriate score, as the score returned is influenced by the number of words in the grammar file, and could be influenced on if they sound similar to other words/phrases in the grammar file.

Detecting Digits and Speech

Often, you will want to allow users to enter digits or use Speech. This code example, shows how to do so. When using Speech or Digits, you will want to get the termination code, and depending on what the termination code is you will need to handle accordingly. In the example above, if the user presses a digit, I will get a terminationcode of “Digit” or “MaximumDTMF”. Alternatively, if the user speaks, the terminationcode will be “Speech”.  VoiceResource.MaximumDigits Property

This is a very simple example of using Speech Recognition technology. If there is something that you would like to develop, but aren’t sure how to start, or have questions, feel free to contact us at [email protected] .

For a deeper dive into the Voice Elements Classes, visit VoiceResource Properties on our Developer Help site.

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August 27th, 2019

How to build a voice assistant with open source Rasa and Mozilla tools

Justina Petraitytė

Platforms like Google Assistant makes it easy to build custom voice assistants. But what if you wanted to build and assistant that runs locally and ensures the privacy of your data? You can do it using open source Rasa, Mozilla DeepSpeech and Mozilla TTS tools. Check out this tutorial to find out how.

With platforms like Google Assistant and Alexa becoming more and more popular, voice-first assistants are destined to be the next big thing for customer interactions across various industries. However, unless you use hosted of-the-shelf solutions, development of voice assistants come with a whole new set of challenges that go beyond NLU and dialogue management - in addition to those, you need to take care of speech-to-text, text-to-speech components as well as the frontend. We touched on the voice topic some time ago when we experimented with building a Rasa-powered Google Assistant . Leveraging platforms like Google Assistant removes the hurdle of implementing the voice processing and frontend components, but it forces you to compromise on the security of your data and the flexibility of the tools you use. So, what options do you have if you want to build a voice assistant that runs locally and ensures the security of your data? Well, let's find out. In this post, you will learn how you can build a voice assistant using only open source tools - from backend, all the way to the frontend.

• Tools and software overview
• The Rasa assistant
• Implementing the speech-to-text component
• Implementing the text-to-speech component
• Putting it all together
• What's next?
• Summary and resources

1.Tools and software overview

The goal of this post is to show you how you can build your own voice assistant using only open source tools. In general, there are five main components which are necessary to build a voice assistant:

• Voice interface - a frontend which users use to communicate with the assistant (web or mobile app, smart speaker, etc)
• Speech-to-text (STT) - a voice processing component which takes user input in an audio format and produces a text representation of it
• NLU - a component which takes user input in text format and extract structured data (intents and entities) which helps and assistant to understand what the user wants
• Dialogue management - a component which determines how an assistant should respond at specific state of the conversation and generates that response in a text format
• Text-to-speech (TTS) - a component which takes the response of the assistant in a text format and produces a voice representation of it which is then sent back to the user

While open source Rasa is a rather obvious choice for NLU and dialogue management, deciding on STT and TTS is a more difficult task simply because there aren't that many open source frameworks to choose from. After exploring the currently available options: CMUSphinx, Mozilla DeepSpeech, Mozilla TTS, Kaldi, we decided to go with Mozilla tools - Mozilla DeepSpeech and Mozilla TTS . Here is why:

• Mozilla tools come with a set or pre-trained models, but you can also train your own using custom data. This allows you to implement things quickly, but also gives you all the freedom to build custom components.
• In comparison to alternatives, Mozilla tools seem to be the most OS agnostic.
• Both tools are written in Python which makes it slightly easier to integrate with Rasa.
• It has a big and active open source community ready to help out with technical questions.

What is Mozilla DeepSpeech and Mozilla TTS? Mozilla DeepSpeech is a speech-to-text framework which takes user input in an audio format and uses machine learning to convert it into a text format which later can be processed by NLU and dialogue system. Mozilla TTS takes care of the opposite - it takes the input (in our case - the response of the assistant produced by a dialogue system) in a text format and uses machine learning to create an audio representation of it.

NLU, dialogue management and voice processing components cover the backend of the voice assistant so what about the frontend? Well, this is where the biggest problem lies - if you search for the open source voice interface widgets, you will very likely end up with no results. At least this is what happened to us and that's why we developed our own Rasa voice interface which we used for this project and are happy to share with the community!

So, to summarise, here are the ingredients of the open source voice assistant:

• Mozilla DeepSpeech
• Mozilla TTS
• Rasa Voice Interface

2. The Rasa assistant

For this project, we are going to use an existing Rasa assistant - Sara . It's a Rasa-powered open source assistant which can answer various questions about the Rasa framework and help you get started. Below is an example conversation with Sara:

Here are the steps on how to set Sara up on your local machine:

• Clone the Sara repository:
• Install the necessary dependencies:
• Train the NLU and dialogue models:
• Test Sara on your terminal:

To turn Sara into a voice assistant we will have to edit some of the project files in the later stages of the implementation. Before we do that, let's implement the TTS and STT components.

3. Implementing the speech-to-text component

Let's implement the speech-to-text component - Mozilla DeepSpeech model. Check out this blogpost by Rouben Morais to learn more about how Mozilla DeepSpeech works under the hood. Mozilla DeepSpeech comes with a few pre-trained models and allows you to train your own. For the sake of simplicity we use a pre-trained model for this project. Here are the steps for how to setup the STT on your local machine:

• Install mozilla deepspeech:
• Download a pre-trained text-to-speech model and unpack it in your project directory:

After running the commands above, you should have a directory called deepspeech-0.5.1-models created in your project directory. It contains the files of the model.

• Test the model.

The best way to check if the component was set up correctly is to test the model on some sample audio inputs. The script below will help you to do that:

• Function record_audio() captures a 5 second audio and save it as a test_audio.wav file
• Function deepspeech_predict() loads a deepspeech model and passes a test_audio.wav file to make a prediction on how the voice input should look like in a text format

Run the script using the command below and once you see a message ' Recording... ' pronounce a sentence you would like to test the model on:

In the next part of this post you will learn how to set up the third piece of the project - the text-to-speech component.

4. Implementing the text-to-speech component

To enable the assistant to respond with voice rather than a text, we have to set up the text-to-speech component which will take the response generated by Rasa and convert it into a sound. For that, we use Mozilla TTS . Just like Mozilla DeepSpeech, it comes with pre-trained models, but you can also train your own models using custom data. This time we will use a pre-trained TTS model as well. Here's how to set up the TTS component on your local machine:

• Clone the Mozilla TTS repository:
• Install the package:
• Download the model:

In your Sara directory, create a folder called _tts_mode_l and place the model files downloaded from here (you only need config.json and best_model.th.tar )

• Test the component:

You can use the script below to test the text-to-speech component. Here's what the script does:

• Function load_model() loads the tts model and prepares everything for processing
• Function tts() takes the text input and creates an audio file test_tts.wav

You can change the sentence variable with a custom input which you would like to test the model on. Once the script stops running, the result will be saved in the test_tts.wav file which you can listen to to test the performance of the model.

At this point you should have all of the most important components running on the local machine - Rasa assistant, speech-to-tech and text-to-speech components. All that is left to do is to put all these components together and connect the assistant to the Rasa voice interface. Learn how you can do it in the next step of this post.

5. Putting it all together

To put all the pieces together and test the voice assistant in action we need two things:

• Voice interface
• A connector to establish the communication between the UI and the backend (Mozilla and Rasa components)

Let's set up the Rasa voice interface first. Here is how to do it:

• Install npm and node following the instructions provided here .
• Clone the Rasa Voice UI repository:
• Install the component:

Once you run the command above, open a browser and navigate to https://localhost:8080 to check if the voice interface is loading. A jumping ball indicates that it has loaded successfully and is waiting for the connection.

To connect the assistant to the interface you need a connector. The connector will also determine what happens when the user says something as well as how the audio response is passed back to the frontend component. To create a connector, we can use an existing socketio connector and update it with a few new components:

• SocketIOInput() class event 'user_utter' is updated to receive the audio data sent as a link from the Rasa voice interface and save it on disc as a .wav file. Then, we load the Mozilla STT model to convert the audio into text representation and pass it to Rasa:
• SocketIOutput() class gets a new method _send_audio_message() which retrieves a response predicted by the Rasa dialogue management model in a text format, loads the Mozilla TTS model which then converts text into an audio format and sends it back to the frontend.

Below, you can find a full code of the updated connector:

Save this code in your project directory as a socketio_connector.py.

The last thing that needs to be set before you can give it a spin is a connector configuration - since we built a custom connector, we have to tell Rasa to use this custom connector for receiving user inputs and sending back the responses. To do that, create a credentials.yml file in Sara's project directory and provide the following details (here socketio_connector is the name of the module where the custom connector is implemented while SocketIOInput is the name of the input class of the custom connector):

And that's it! All that is left to do is to launch the assistant and have a conversation with it. Here's how to do it:

• While in your working directory, start a Rasa assistant on a server:
• Start the Rasa custom action server:
• One of the Sara's components is a DucklingHTTPExtractor component. To use it, start a server for the duckling component by running:
• Start a simple http server for sending the audio files to the client:

If you refresh the Rasa voice interface in your browser, you should see that an assistant is ready to talk:

Click start, and have a conversation with a voice assistant built using only open source tools!

6. What's next?

Development of voice assistants comes with a whole new set of challenges - it's not just about the good NLU and dialogue anymore, you need good STT and TTS components and your NLU has to be flexible enough to compensate for the mistakes made by STT. If you replicate this project you will notice that the assistant is not perfect and there is a lot of room for improvement, especially at the STT and NLU stage. How can you improve it? Here are some ideas for you:

• Pre-trained STT models are trained on a quite generic data which makes the model prone to mistakes when used on more specific domains. Building a custom STT model with Mozilla DeepSpeech could lead to a better performance of STT and NLU.
• Improving the NLU could potentially compensate for some of the mistakes made by STT. A rather simple way to improve the performance of the NLU model is to enhance the training data with more examples for each intent and to add a spellchecker to the Rasa NLU pipeline to correct some smaller STT mistakes.

7. Summary and resources

At Rasa, we constantly look for ways to push the limits of the tools and software that empower developers to build great things. By building this project, we wanted to show you that you can use Rasa to build not only text, but also voice assistants and inspire you to build great applications without compromising on security and the flexibility of the tools you use. Have you built a voice assistant with Rasa? What tools did you use? Share your experience with us by posting about it on the Rasa community forum.

• Rasa Community Forum
• Rasa documentation
• Rasa voice interface

Acknowledgments

Rasa Voice Interface was developed by INTEGR8 dev team and is maintained by Rasa.

Make your Speech Recognition System Sing

What You Can Gain From Using Voice Recognition Datasets for Machine Learning

Your data can be the difference between an efficient and cost-effective voice recognition system and one that doesn’t work very well. When it comes to machine learning, one of the most important components for a successful launch and return on investment is data. If you’re planning to build a voice recognition system or conversational AI, you’ll need a big speech recognition dataset. Pre-labeled datasets could be the solution.One of the struggles that many companies face today is how to get the data they need and to ensure that they’re getting high-quality data, which will help them build a successful machine learning model.

How Speech Recognition Datasets Can Benefit Your Organization

The importance of pre-labeled datasets is in how they can benefit your company or organization. Pre-labeled datasets allow organizations to get to the deployment phase faster and with spending less money.When you opt for a pre-labeled dataset instead of building your own or purchasing a custom dataset, you can spend the majority of your team’s time and money on building and training your speech recognition model. When you’re less focused on collecting and labeling data, all of your resources can be spent on building and training your model, which results in a higher quality, better model. When you have a better model, you get a higher return on your investment, with better results and better insights.No matter where you are in the world, you can benefit from pre-labeled data at your organization. Pre-labeled datasets offer better data at a more affordable cost, allowing more organizations to effectively build and launch speech recognition machine learning models.

Pre-labeled Datasets in Practice

An example of a pre-labeled dataset in practice comes from MediaInterface . While MediaInterface has been working with healthcare-related institutions and collecting data for over 20 years, the vast majority of their data is in German, which is the language spoken in their primary markets.When MediaInterface wanted to expand to France, they needed data. Another hurdle they faced is that much of the place name data was redacted due to GDPR protections and guidelines. That’s when MediaInterface came to Appen.Using one of Appen’s pre-labeled datasets, MediaInterface was able to get 21,000 French names and 14,000 place names in their dataset. This data helped them to launch efficiently in a new market.

Through the use of a pre-labeled dataset, MediaInterface was able to efficiently launch in a new market while not incurring large costs.

Pre-Labeled Speech Recognition Datasets

Pre-labeled datasets are a newer option for companies that don’t have the time or resources to build their own custom dataset. A pre-labeled speech recognition dataset is a set of audio files that have been labeled and compiled for being used as training data for building a machine learning model for use cases such as conversation AI.The beauty of pre-labeled datasets is that they’re built and ready to go. Before the use of pre-labeled datasets, companies had to either build their own dataset from scratch, collecting and labeling each data point, or hire a company to build the dataset for them. Both building your own and buying a custom dataset are hard on company resources, costing money or time.Now, there are a wealth of options out there for pre-labeled speech recognition datasets. When it comes to pre-labeled datasets, you’ll find two options: for purchase or open source. Both options have their place, you’ll just have to find the right one for your company.Across the internet, you’ll find a dozen or more resources for finding and purchasing pre-labeled speech recognition datasets. At Appen, we have over 250 datasets, which include audio datasets with over 11,000 hours of audio and 8.7 million words across 80 different languages and multiple dialects.

Examples of Pre-Labeled Datasets Available for Purchase

Pre-labeled datasets, whether you’re getting them from us or another vendor, are a great resource for jumpstarting an AI or machine learning project. Because a pre-labeled dataset is already built, you can jump directly to training your model with no delays. Using a pre-labeled dataset is cost-effective and speeds up your time to deployment. While building or buying your dataset would take an average of eight to twelve weeks from start to finish, you can purchase and receive a pre-labeled dataset in days to a week.There are a number of online resources for finding pre-labeled speech recognition datasets. You can start on our website and filter for audio datasets or check out any of the other paid or open-source dataset resources we suggest below.Each of the below databases includes speech audio files and text transcriptions that you can use to build up your Speech Corpora with the utterances from a variety of speakers in a number of different acoustic conditions, making for high-quality, varied data.

Appen: Arabic From Around the World

Our repository of pre-labeled speech recognition datasets includes a number of different sets for Arabic being spoken around the world. We have datasets of Arabic speakers in Egypt, Saudi Arabia, and the UAE.

Appen: Baby Crying

One of our newest pre-labeled audio datasets is of pre-recorded and annotated baby sounds. In these audio files, you’ll hear different baby cries and sounds. This dataset would be great for training AI models to recognize different infant sounds and types of cries, which would then be able to alert parents.

Appen: Less Common Languages

One of the major issues with the pre-labeled datasets you’ll find on the market is that they focus on European languages or English. Our repository of pre-labeled datasets includes less common languages, such as:

• Bahasa Indonesia
• Bengali (Bangladesh)
• Bulgarian (Bulgaria)
• Central Khmer (Cambodia)
• Dari (Afghanistan
• Dongbei (China)
• Uygur (China)
• Wuhan Dialect (Chinese)

This is just a small selection of the languages and dialects that you’ll find in our over 100 speech recognition pre-labeled datasets.

Appen: Non-Native Chinese speakers

Another dataset included in our pre-labeled product, speech recognition repository is a dataset of non-native Chinese speakers speaking in Chinese. This type of dataset can be great for creating a wider variety of speakers and accents in your training dataset which will result in a better-performing machine learning model.This dataset includes 200 hours of foreigners speaking Chinese. Speakers come from countries such as:

• Kuala Lumpur
• Philippines
• South Africa
• United States

While this dataset is quite inclusive, it doesn’t include data from South Korea or Brazil. There’s also no data recorded by minors. To protect privacy, all sensitive and personal information has been scrubbed.

Appen: Languages Spoken Across the Globe

Another unique feature of our pre-labeled datasets is that you can get datasets for one language but spoken in different regional dialects. For example, German isn’t only spoken in Germany. If you’re creating a machine learning model for German speakers, your data will be incomplete if you have a dataset that Features only German speakers from Germany.These around the world datasets include:

Our pre-labeled datasets have a comprehensive collection of different languages, but also a variety of dialects.

LibriSpeech

A non-Appen pre-labeled dataset that we highly recommend is that from LibriSpeech . This dataset was put together as part of the LibriVox project which includes data compiled from people reading audiobooks. The dataset includes about a thousand hours of speech data that’s been segmented and labeled.

M-AI Labs Speech Dataset

Another common issue with speech recognition datasets is they’re not representative of gender, they often feature male voices heavily and have few female voices, which can cause gender biases in the abilities of voice assistants and other machine learning models.That’s why we recommend M-AI Labs Speech Dataset in our list of pre-labeled datasets. It has almost 1000 hours of audio paired with transcriptions and represents male and female voices across several languages.There are a number of different sources where you can find high-quality, pre-labeled datasets to use to train your machine learning model and get to the deployment stage efficiently.

Open Source Speech Recognition Datasets

Using a pre-labeled dataset to train your speech recognition machine learning model is an efficient and cost-effective way to get to deployment. But, if you’re on a really tight budget for development, there’s another, even less expensive option out there for you.Open source speech recognition datasets are available and free to use. These open datasets include audio files and text transcriptions that have been put together by various groups or people. You can find open-source datasets from a variety of different sources online. You may have to spend a little extra time researching to find an open-source dataset and verifying its quality, but the extra time can save you quite a bit of money.Here are a few open-source speech recognition datasets we recommend trying.

A great place to find open-source speech recognition datasets is Kaggle . Kaggle is an online community where data scientists and machine learning engineers gather to share data, ideas, and tips for building machine learning models. On Kaggle you can find over 50,000 open-source datasets for a wide variety of use cases.

Common Voice

Another great open-source speech recognition dataset comes from Common Voice . This dataset consists of over 7000 hours of speech in over 60 different languages. What sets this dataset apart from others is that includes metadata tags for age, sex, and accent which can help you to train your machine learning model and create accurate results.

Coming from the National Institute of Korean Language, homink is a speech corpus that includes 120 hours of people speaking Korean. This specialized open-source dataset is a great resource for those working on machine learning projects and wanting to include the Korean language.

siddiquelatif

Another unique open-source dataset is siddiquelatif . This dataset includes 400 utterances in Urdu, which have been collected from Urdu talk shows. The utterances represent both male and female speakers and a variety of emotions.Open source datasets can sometimes lack in size and quality when compared to pre-labeled datasets that are available for purchase, but they’re a great option if you’re looking to launch your machine learning project on a tight budget. With a little research and digging you can find high-quality open-source speech-recognition datasets.

Potential Problems with Speech Recognition Data

One of the critical elements of machine learning model training data is quality. If you put high-quality training data into your machine learning model, you’ll get high-quality results out. If you’re not using high-quality data, your results won’t be as good.While high-quality data may seem like a nebulous concern, there are a few big problems to watch out for when examining and choosing a pre-labeled dataset.

Overlooking Less Common Languages

Many pre-labeled datasets aren’t representative of all languages or even of the most commonly spoken languages. When looking through pre-labeled datasets online, you’ll notice that there are certain languages that it’s more difficult to find datasets for. This language bias can make creating and training a representative machine learning model a struggle.While this bias exists, you can also find a number of programs working towards correcting the bias. For example, the open-source dataset homink and siddiquelatif represent Korean and Urdu, respectively.Another database for under-represented languages comes from The Computer Research Institute of Montreal. This database makes it easier to access recordings of Indigenous languages being spoken and to create reliable transcriptions. The indigenous languages included in this database are:

While you might be able to find other datasets of Indigenous languages being spoken, what makes this dataset unique is the annotations and indexing. This database can be searched using keywords, perform speech segmentation, and use language labeling tools. This type of high-quality dataset makes it possible to create automatic speech recognition for Indigenous languages.It’s important when looking for pre-labeled datasets and building speech recognition machine learning models to be aware of potential bias. Be looking for bias in your datasets and try to avoid building it into your model.

Using Biased Data

Another major problem with pre-labeled datasets is biased data. When it comes to data and speech recognition machine learning models, there are a number of different forms of bias. The two most common forms of bias are gender and racial bias. In general, machine learning models on the market are less capable of recognizing speech from women and people of color. And while speech recognition software has made progress in recent years, it’s not enough.A 2020 Stanford University study looked at speech-to-text transcriptions from 2000 voice samples for services from Amazon, IBM, Google, Microsoft, and Apple. They found that those speech-to-text services misidentified words from Black speakers at nearly double the rate of misidentification of words spoken by white speakers. This bias shows a lack of data diversity and a bias in training data. To deploy a successful machine learning model, it’s critical that your data be representative of the whole population, not just a portion of the population.Racial bias isn’t the only bias that speech recognition machine learning models are facing. Research has also found gender bias in speech recognition models. Research done by Dr. Tatman and published in the North American Chapter of the Association for Computational Linguistics found that Google’s speech recognition software was 13% more accurate for men than women. This difference may seem small, but it’s important to note that Google has the least gender bias when compared to Bing, AT&T, WIT, and IBM Watson.Like any machine learning model, speech recognition models learn by being trained on a large amount of data. This is why the quality of your training dataset is so critical to deploying a successful machine learning model. If you use biased, low-quality data, your model will produce biased, low-quality results. The system will mimic the biases found in the data. Even when these biases are unintentional, they can still be harmful to users and to the company’s bottom line. The more diverse your data, the less biased your machine learning model.

How to Avoid Bias in Speech Recognition Data

When building a machine learning model, it’s critical to use unbiased training data to ensure the success of your model and a high return on your investment. Eliminating and avoiding bias in your machine learning model isn’t a one-and-done step. Getting rid of bias requires attention to detail, planning, and thoughtfulness.A few small examples of how you can lower bias in your machine learning models include:

• Provide implicit bias training to improve bias awareness. Resources such as Harvard’s Project Implicit and Equal AI provide programs and workshops.
• Search for less biased data and don’t settle for the first pre-labeled dataset you find.
• Investigate data providers and review their writing on bias in AI
• Use a diverse group of testers to catch bias before you launch your machine learning model
• Acknowledge that bias is part of our world and part of our data

As machine learning models become a bigger part of our everyday lives, it’s critical that the technology be able to be used by everyone — equally.

Create AI That Learns and Adapts

A big shift in machine learning models that can help to eliminate bias is building models that learn and adapt as they’re used. When machine learning models can learn as they go, they’re better able to adapt to different subsets and groups of people and environments, which makes them more adaptable and less biased.An example of this in action comes from Verbit , an in-house AI that gets smarter with each use. Users have the ability to upload a glossary of terms, including speaker names and complex words so that the machine learning tool can recognize those words more easily and create more accurate transcriptions. As well, the model can learn from corrections that are put in later when the transcription is reviewed by humans.This back-and-forth between human and model allows the model to constantly be learning, changing, and adapting. This makes for a less biased model that can be used by everyone. Like this example, AI should adapt to the user, not the user adapting to the AI. There’s no need to settle for mediocre results when machine learning models have the capability to continuously learn and improve the more people it interacts with.

Diversity in Hiring

When it comes to bias, you can’t just play the short game. Bias is a part of our culture and to eliminate it in our technology, we have to lessen it in our communities. This means making changes to hiring practices.When your team is more representative, your machine learning model and data will be more representative. The more diversity you have sitting at the table reviewing projects, decisions, and data, the less likely you are to build implicit bias into your machine learning models. We naturally, and understandably, build for our own. But, that doesn’t make for the best products or models. To build the best products that work for everyone, it’s critical to involve more diverse people in the process. This starts in your hiring practices.

How Appen Can Help

If you’re looking for a high-quality, pre-labeled dataset to help train your speech recognition model, Appen has what you need. We have a wide variety of pre-labeled datasets that can be used for various use cases. With datasets representing over 80 different languages and dialects, you’ll be able to find just the right data that you need.At Appen, we also strive to provide representative, unbiased data.No matter what you’re looking for, we have the resources to help you. Choose from our pre-labeled datasets for speech recognition, purchase a custom-made speech recognition dataset from us, or, if we don’t have it, let us help you find the right pre-labeled dataset for your use case. From start to finish, we have the tools you need to deploy your speech recognition machine learning model.Learn how a pre-labeled dataset could save you time and money .

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• Speech Recognition Software

The Best 7 Free and Open Source Speech Recognition Software Solutions

The well-accepted and popular method of interacting with electronic devices such as televisions, computers, phones, and tablets is speech. It is a dynamic process, and human speech is exceptionally complex. The speech recognition engines offer better accuracy in understanding the speech due to technological advancement. A  study  indicates that from 2019 to 2025, the global speech and voice recognition market can reach $26.79 billion.

Developers integrate speech recognition into the applications as they are useful in understanding what is said. In smart watches, household appliances and in-car assistants’ speech recognition are used. The Speech Recognition Software has to deal with a variety of speech patterns and individuals’ accents.

Here in this article, you will come to know about the working, benefits and best free and open source speech recognition software solutions available in the market.

What is Speech Recognition?

The technology-speech recognition permits spoken input into systems. It is considered an ability of a machine to recognize words and phrases in spoken language and then change it to the machine-readable format. In simple words, it means that it is a computer program that is taught to take the input of human speech which is then interpreted and then finally written out into the text.

Benefits of using Open Source Speech Recognition Software

• Assist companies to save time and money by mechanizing business processes. On phone calls, it provides instant sights on what’s happening.
• More cost-effective as the software performs the task of speech recognition and  transcription faster and more accurately than a human.
• The cost of speech recognition and transcription software is less per minute and is measured more accurately than a human performing at the same rate.
• Easy to use and readily available. In computers and mobile devices, speech recognition software is frequently installed in computers and mobile devices that allow for easy access.

Best 7 Free and Open Source Speech Recognition Software Solutions:

1  simon.

Simon is considered very flexible speech recognition software meant for the free and open source. It allows customization for any applications wherever speech recognition is required. It can work with any dialect and is not bound to any language. It can replace the mouse and keyboard.

Simon makes use of KDE libraries, CMU SPHINX or Julius together with the HTK and it runs on Windows and Linux. One can open the URLs and programs, type configurable text snippets, control the mouse and keywords and simulate shortcuts.

It turns audio into text and allows voice commands. You can check out Simon if you would like to talk to your computer. 

(Source : Simon )

• From the input, it can execute all sorts of commands. It receives information from the server Simond.
• Command-and-control solutions are appropriate for disabled people.
• The same version of Simon can be used with all languages and dialects because of its architecture. If required then you can even mix  languages within one model.
• An exclusive do-it-yourself approach is provided to speech recognition by Simon. To create language and acoustic models from scratch, it provides an easy to use end-user interface.
• From other users, the end-user can easily download established use cases and can share his or her cases.
• It controls many different types of software including web browsers, media centers, email clients by making use of few words like “left,” “right,” “ok,” “stop” etc.

2  Kaldi

Kaldi is an open source speech recognition software that is freely available under the Apache License. In John Hopkins University, the development fired up at a workshop in 2009 that called “Low Development Cost, High-Quality Speech Recognition for New Languages and Domains.”

On May 14, 2011, the code for Kaldi was released after working on the project for a few years. Quickly Kaldi gained a reputation for its ease to work with. It is written in C++ and is intended to be used mainly for acoustic modeling research.

(Source: Kaldi )

• Supports full covariance structures along with Gaussian mixture modules along with diagonal.
• Holds MMI and boosted MMU.
• Code-level integration with Finite State Transducers accumulates against the OpenFst toolkit.
• Possesses tools for changing LMs in the standard ARPA format to FSTs.
• Enjoys the support of the general linear algebra along with a matrix library that wraps standard Basic Linear Algebra Subroutines and Linear Algebra Package routines.
• An extensible design that features-space discriminative training.
• Offers complete recipes and deep neural networks.
• Use maximum likelihood linear regression (MLLR) to support model-space adaptation and use feature-space MLLR to support feature-space adaptation.

3  CMUSphinx

The short form of CMUSphinx is Sphinx. It is a speaker-independent large vocabulary continuous speech recognizer that is released under the BSD style license. This is a group of speech recognition systems which is developed by the Carnegie Mellon University.

The number of packages is found in this open source and free speech recognition software. Each is designed for different types of tasks and applications.

• Pocketsphinx - A lightweight speech recognition engine which is written in C. It is specially designed for handheld and mobile devices.
• Sphinx base - Holds the necessary libraries which are shared by the CMU Sphinx trainer, Sphinx decoders, some common utilities that influence acoustic features and audio files.
• Sphinx4 -  Speaker independent, a state-of-the-art, continuous speech recognition system that is written in the Java programming language.
• Sphinxtrain - Open source acoustic model trainer of Carnegie Mellon University.

(Source : CMUSphinx )

Toolkit Features:

• For low-resource platforms, the CMUSphinx tools are designed. Efficient speech recognition is possible because of the state of the art speech recognition algorithms.
• Holds a flexible design that centers on realistic application development and not on research.
• Ample of tools meant for the speech recognition related purposes like keyword spotting, pronunciation evaluation, and alignment.
• Encourage various languages like Mandarin, Dutch, German, Russian, English, and French. Enjoys the ability to build models for other languages.

4  Mozilla

An open source voice recognition tool is released by the Mozilla that it states is “close to the human level performance.” It is free speech recognition software for developers to plug into their projects. Mozilla Senior Vice President of Emerging Technologies Sean White wrote in a blog post that “We at Mozilla believe technology should be open and accessible to all, and that includes voice.”

(Source: Mozilla )

• Project Common Voice by Mozilla is a campaign that asks people to donate recordings of their voices to an open repository.
• Speech algorithms enable developers to create speech interfaces that use considerably simplified software architectures.
• Mozilla DeepSpeech is an open source Tensorflow-based speech-to-text processor that has reasonably high accuracy.
• DeepSpeech, the speech recognition tool has a remarkable per-word error rate of near about 6.5%.
• Uses open source code algorithms and TensorFlow machine learning toolkit to build its STT engine.
• Better awareness of privacy concerns and is considered more powerful hardware.
• The DeepSpeech project is also available in many languages such as Python (3.6); which allows having its working in seconds.

5  Julius

Julius is measured as the free high-performance and two-pass large vocabulary continuous speech recognition decoder software (LVCSR) for speech-related developers and researchers. It carries out multi-model decoding, a recognition utilizing some LMs and AMs concurrently with a single processor. At run time it supports the “hot plugging” of arbitrary modules.

To deal with other toolkits like HTK, CMU-Cam SLM, this open source speech recognition software adopts standard formats. Various types of speech recognition system can be built by putting up their own models and modules that are apt for the task. Both acoustic models and language models are pluggable. The various applications offer the speech recognition capability as the core engine is put into practice as an embeddable library. The user can extend the engine as the recent version supports plug-in capability.

(Source: Julius )

• For work area less than 32Mbytes memory is required.
• Precise, hi-speed and real-time recognition based on 2-pass strategy.
• Supports LM of grammar, isolated words and N-gram.
• In ARPA standard format any LM and in HTK ascii hmmdefs format AM is used.
• Can set various search parameters due to high configurability.
• In English/Japanese there is full source code documentation and manual.
• For microphone and network, input works as the on-the-fly recognition.
• Short pauses delimit input. Enjoys successive decoding.
• Input rejection is GMM based. Word-graph and N-best output. Confusion network output.
• On word, phoneme and state level there is forced alignment.
• Confidence scoring, control API and server mode.
• For tuning the performance has many search parameters.
• For a result, output holds character code conversion.
• Long N-gram support and run with forward/backward N-gram only.
• In a single thread enjoys the arbitrary multi-model decoding.
• Word acknowledgment is speedy isolated.
• LM function is user-defined.

6  Dictation Bridge

Dictation Bridge is a free and open source dictation solution for NVDA and Jaws. It is a gateway between NVDA, Jaws screen readers, either Dragon Naturally Speaking or Windows Speech Recognition. Both Windows Speech Recognition and Dragon can be controlled by Jaws users.

In Dragon and Windows Speech Recognition (WSR) it can echoes back the dictated text. It serves as an extensive collection of verbal commands that can control screen readers and perform a variety of other tasks with Dragon products.

(Source: Dictation Bridge )

• Speech only supports of the WSR correction box and help to control NVDA from Dragon and WSR. Only Dragon commands have been written at this time.
• From Dragon, it possesses the command NVDA by voice.
• While using Dragon, a verbal notification of the microphone status comes. No support needs to be created as WSR has this built-in feature.
• Provides all the features for both NVDA and JAWS in a fully featured dictation plug-in or set of configurations.
• First ever dictation solution for screen readers that takes in a wide-ranging collection of verbal commands which users use to control the screen reader.
• Can be translated into any of the 35 languages that are supported by Windows Speech Recognition and more than 43 languages hold up by NVDA.
• This free and libre open source software (FLOSS) holds the highest quality documentation.
• Affords the community the freedom to modify, learn from, add to, repurpose or do anything else. 
• Capable of handing off the functionality between screen readers if both NVDA and Jaws versions are installed.

7  Mycroft

Mycroft is the name of a set of software and hardware tools that make use of natural language processing and machine learning which offers an open source voice assistant. It is the private and open voice solution for consumers and enterprise. This open source voice assistant can be extended and expanded to the limits of the imagination.

It runs anywhere - on a desktop computer, inside an automobile or on a Raspberry Pi. It can be freely remixed, extended and improved. It may be used in anything from a science project to an enterprise software application.

(Source: Mycroft )

• The code used by Mycroft can be examined, customized, copied and contributed back to the Mycroft community for everyone to enjoy.
• It uses opt-in privacy that means it will only record what is said to Mycroft with explicit permission.
• Runs on a wide range of software platforms and hardware. You can run Mycroft on the devices as per the choice.
• Works as an active, engaged and helpful community.
•  Holds messaging and reminder function.
• Enjoys audio record, speech recognition, speech-to-text, text-to-speech, machine learning, software library, natural language processing, and Linux OS.

Apart from the in-depth description of the best free and open-source speech recognition software, you can also try Braina Pro , Sonix , Winscribe Speech Recognition , Speechmatics . Dragon NaturallySpeaking is one more popular speech recognition software which you can explore here.

Dragon NaturallySpeaking

By merely speaking, you can improve the documentation productivity with Dragon NaturallySpeaking tool. You have to talk, and your words will appear on the screen. Your computer will obey your commands. Whether your business is of financial services, education, or healthcare; Dragon NaturallySpeaking software will provide you appropriate solutions to your business needs.

This software is 99% accurate and is three times faster than typing. This software allows individuals to create and share high-quality documentation and helps in simplifying the complex workflows. You can become more productive by using this software. 

(Source: Dragon NaturallySpeaking )

• Assists in several daily activities like sending emails, web surfing, dictating homework assignments. 
• Working individuals and small businesses can create and transcribe documents through dragon professional.
• Synchronization is possible with Dragon Anywhere.
• It streamlines the legal documentation with dragon legal individual.
• With speed and accuracy, it controls the computer by voice.

Conclusive thought

In this article, you will get comprehensive information regarding open source speech recognition solutions. From the list, you can choose one of the most promising free open source speech recognition software that can efficiently meet your demands and requirements.

The open and free source speech recognition software can construct the speech recognition application that requires advanced speech processing techniques. All these techniques are realized by specialized speech processing software.

Depending on the open source speech recognition software you can make use of speech recognition to speak to your computer, read out documents, open, edit and send emails. The free speech recognition software is available in many forms like web, mobile, and desktop.  Make sure that whatever speech recognition software you choose, it should be precise in identifying the words you speak and allow you to place in formatting options like symbols and special characters.

If you are looking for more options on the best speech recognition software to construct your speech recognition application, then indeed this article will provide you immense and in-depth knowledge and understanding on the same.

In case before now you have tried out any of the above listed speech recognition software, then feel free to share your precious views and feedback on the same.

If you are looking for other software categories, then click here .

Andrea Hernandez is a tech blogger and content marketing expert. She writes about disruptive tech trends counting blockchain, artificial intelligence and Internet of Things. Presently, as a senior writer, she is associated with GoodFirms , a pioneering B2B research, review and rating platform. Follow her on social media for valuable information on the software.

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3 best practices for building speech recognition models

%t min read | by Dylan Fox

Photo by  Miguel Á. Padriñán  from  Pexels

Automated speech recognition ( ASR ) has improved significantly in terms of accuracy, accessibility, and affordability in the past decade. Advances in deep learning and model architectures have made speech-to-text technology part of our everyday lives—from smartphones to home assistants to vehicle interfaces.

Speech recognition is also a critical component of industrial applications. Industries such as call centers, cloud phone services, video platforms, podcasts, and more are using speech recognition technology to transcribe audio or video streams and as a powerful analytical tool. These companies use state-of-the-art speech-to-text API s to enhance their own products with features like speaker diarization (speaker labels), personally identifiable information (PII) redaction, topic detection, sentiment analysis, profanity filtering, and more.

Many developers are experimenting with building their own speech recognition models for personal projects or commercial use. If you're interested in building your own, here are a few considerations to keep in mind.

Choose the right architecture

Depending on your use case or goal, you have many different model architectures to choose from. They vary based on whether you need real-time or asynchronous transcription, your accuracy needs, the processing power you have available, additional analytics or features required for your use case, and more.

Open source model architectures are a great route if you're willing to put in the work. They're a way to get started building a speech recognition model with relatively good accuracy.

[ Learn how to alleviate technical debt —in time and money—through IT modernization. ]

Popular open source model architectures include:

Kaldi: Kaldi is one of the most popular open source speech recognition toolkits. It's written in C++ and uses CUDA to boost its processing power. It has been widely tested in both the research community and commercially, making it a robust option to build with. With Kaldi, you can also train your own models and take advantage of its good out-of-the-box models with high levels of accuracy.

Mozilla DeepSpeech: DeepSpeech is another great open source option with good out-of-the-box accuracy. Its end-to-end model architecture is based on innovative research from Baidu, and it's implemented as an open source project by Mozilla. It uses Tensorflow and Python, making it easy to train and fine-tune on your own data. DeepSpeech can also run in real time on a wide range of devices—from a Raspberry Pi 4 to a high-powered graphics processing unit.

Wav2Letter: As part of Facebook AI Research's ASR toolkit, Wav2Letter provides decent accuracy for small projects. Wav2Letter is written in C++ and uses the ArrayFire tensor library.

CMUSphinx: CMUSphinx is an open source speech recognition toolkit designed for low-resource platforms. It supports multiple languages—from English to French to Mandarin—and has an active support community for new and seasoned developers. It also provides a range of out-of-the-box features, such as keyword spotting, pronunciation evaluation, and more.

Make sure you have enough data

Once you've chosen the best model architecture for your use case, you need to make sure you have enough training data. Any model you plan to train needs an enormous amount of data to work accurately and be robust to different speakers, dialects, background noise, and more.

[ Explore important considerations for hybrid cloud, containers, multicloud, and Kubernetes technologies in An architect's guide to multicloud infrastructure . ]

How do you plan to source this data? Options include:

• Paying for training data (this can get very expensive quickly)
• Using public data sets
• Sourcing it from open source audio or video streams
• Using in-person or field-collected data sets

Make sure your data sets contain a variety of characteristics, so you won't bias your model towards one particular subset over another (for example, toward midwestern US speech versus northeastern US speech or towards male speakers versus female speakers).

Choose the right metrics to evaluate the model

Finally, you need to choose the right metrics to evaluate your model.

When training a speech recognition model, the loss function is a good indicator of how well your model fits your data set. A high number means your predictions are completely off, while a lower number indicates more accurate predictions.

However, minimizing loss will only get you so far—you need also to consider the word error rate (WER) of your speech recognition model. This is the standard metric for evaluating speech recognition systems.

WER adds the number of substitutions (S), deletions (D), and insertions (I) and divides them by the number of words (N). The resulting percentage shows your WER—the lower, the better. However, even WER isn't failproof. WER fails to consider elements like context, capitalization, punctuation, and such in its calculation, so always compare normalized transcripts (model versus human) to get the best picture of accuracy.

Build, evaluate, and repeat

By following the steps below, you'll be on your way to building a robust speech recognition model:

• Choose the best model architecture for your use case
• Source enough diverse data
• Evaluate your model effectively

Note that building a speech recognition model is a cyclical process. Once you reach the evaluation stage, you'll often find that you need to go back and retrain your model with more training data or a more diverse training set, as you continually work toward greater accuracy.

Model forums, community boards, and academic research can be great resources to learn more about the latest approaches and trends and to help you solve problems as you work.

Dylan is the Founder and CEO of AssemblyAI, a Y Combinator backed startup building the #1 rated API for Automatic Speech Recognition. Dylan is an experienced AI Researcher, with prior experience leading Machine Learning teams at Cisco Systems in San Francisco. More about me

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Building a Speech Recognition System vs. Buying or Using an API

Rev › Blog › Resources › Other Resources › A.I. & Speech Recognition › Building a Speech Recognition System vs. Buying or Using an API

If your company operates in a domain that requires frequent speech to text transcriptions, you’re probably wondering whether there’s a long term payoff in building your own automated speech recognition system (ASR) vs. buying on-demand access via a service such as Rev.ai .

It’s a tricky question, and the decision may skew very clearly one way or the other if you’re on either of the extreme ends of usage. However, we’ve found that for almost all businesses with typical usage, paying for an on-demand speech recognition service is a much higher value than building your own. Here’s why.

Development Team Costs

The first thing you’ll need to realize about building an ASR is that it’s not a simple task that you can offshore or hand off to inexperienced workers. The technology behind ASR is machine learning, which involves a huge amount of math, data, and software domain expertise.

You’ll typically have to hire more than one person because the chances of finding all of this domain expertise in a single individual is rare, not to mention that even if they have it, building an ASR is not typically a one-man job. You’ll need to hire multiple people to distribute the workload. Usually the people you’ll need to hire to build an ASR system will include at least one of each of a Machine Learning Scientist/Researcher (PhD level), a software engineer for creating APIs and deployment, and a data engineer to help warehouse and manage all the text, audio, and other training data.

The salary range you’ll need to pay for these positions, at least if you want to attract decent engineers, will easily be in the range of $100k-200k per person and more if you want to pay for more experienced people. Therefore, you’re looking at a yearly annual expenditure of at least $300k-600k for a lean, bare-bones development team.

“Well,” you might be thinking, “that’s not so bad. I’ll just pay them for 6 months to 1 year and then I’ll have my ASR system finished for use in perpetuity.”

Not so fast. While it’s a nice dream, the world of software development is inherently messy. Even if such a small team was able to build a high quality ASR system in such a short time frame (highly unlikely), it’s not like it’s something you can just build and then expect to have it run smoothly. Machine learning models like this operate in feedback loops.

There will always be small bugs that arise in production, issues that arise as your service hits greater scale, and new data that gets injected into your model. This last one is crucial – the need to update the model on new data, and the occurrence of phenomena such as model drift , will almost certainly mean that a live production system such as your ASR one will need to be retrained regularly.

All this to say that the development team is not a one-time cost, it’s a crucial and ongoing cost that will most likely exist for the lifetime of your ASR system’s use. So count on an expenditure of at least $300k-600k per year.

Unfortunately, the costs of building an ASR system don’t stop at the development team. Most current, state-of-the-art ASR models are actually deep learning models meaning they’re large neural networks that take tons of data to properly train. Think millions or billions of data points.

That means you’ll need a huge library of audio files (and corresponding text transcripts) to effectively train your model. Unless you’re a search engine giant such as Google, or a dedicated ASR company such as Rev.ai, which has access to years of transcription data logged by its team of 50,000 human transcriptionists, you most likely won’t have access to data at the scale needed to train one of these systems.

Of course, you could go out and gather that data, either by paying for it (think tens of thousands of dollars to license certain datasets), scraping it from the web (thousands of hours of running background scripts), or curate it from your customers (years of back and forth, human interaction).

Clearly, none of these are ideal solutions for a small to medium sized business. Even for a large business, the hassle is often much more than it’s worth. That’s why corporate juggernauts such as Bloomberg, VICE, Loom, and others use Rev to generate their transcriptions.

Infrastructure Costs

Let’s assume for the sake of argument that you do have the data necessary to train a high quality ASR model. You’ll still need the infrastructure to train your model. Here is one current state-of-the-art ASR setup . If you don’t want to read the whole paper, there’s also a nice summary of current state-of-the-art ASR models here . Here’s an excerpt about training the model from that same summary:

The network has 12 residual blocks, 30 weight layers, and 67.1M parameters. Training was done using the Nesterov accelerated gradient with learning rate 0.03 and momentum 0.99. The CNN was also implemented on Torch using the cuDNN v5.0 backend. The cross-entropy training took 80 days for 1.5 billion samples using a Nvidia K80 GPU with a 64 batch size per GPU .

That 80 days of training would be a huge ask for any company, and note that that was only the successful training run. They likely had multiple false starts as well. Note also the dataset size: 1.5 billion samples! Finally, they required a Nvidia K80 GPU to do the training.

This sort of hardware isn’t cheap, although they likely could have sped up the training process by parallelizing across multiple, more powerful GPUs. That brings down the training time but also significantly ups the infrastructure spend. Like all things, it’s a tradeoff, and probably one you don’t want to concern yourself with.

Cost of Buying from a Pre-Built Service or API

Now that we’ve outlined some of the major costs associated with building an ASR system, it’s only fair to compare it to the cost of the alternative: using a service that a dedicated team has already built. Rev.ai operates according to a very simple pricing model . You can either pay as you go for just over 3 cents per minute of audio/video transcribed, or if you are an enterprise client you can get that same service for $1.20 per hour. That’s about a 28% discount.

If you’re like most businesses, you’ll probably only be transcribing maybe a few thousand hours of audio per year. At the $1.20 per hour rate, that’s only a few thousand dollars per year, less than you’d spend to hire a single developer for a single month ! However, even if your usage is extremely high, you’re unlikely to cross the threshold where the cost-benefit analysis turns in your favor in terms of building your own speech recognition system. Let’s take a look again at the cost of building your own system:

And that’s for a fairly minimal system without all the bells and whistles. Remember, most of the state of the art systems had 4 – 10+ authors on their research papers alone, and that’s probably not taking into account the software and other engineers supporting the team but not working directly on the algorithm itself.

So at the upper end of that cost range, you would need to be transcribing 591,666 hours of audio per year for the balance to tip in favor of building your own system. And even at that point, all the headaches of managing a dedicated software team may not be worth it.

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Building speech recognition for a new language from scratch

I know C++ and PHP, I know OOP and usages of Database technologies. I have to make speech recognition software for my own nation Whose symbols are unique but supported by UTF-8. and so far no software company has taken initiative to do so. I need to know what programming language will be perfect to do and which courses should I take to learn the process. I don't like to process the language via SAPI or build in recognition technologies as they are based on English (Problem here the grammar and syntax is so difference- it's Indo-European based). And I want to make it from scratch ( machine level/voice processing- i want to make sound that processed directly be parsed to my symbols (no English transformation )). Hope you will understand cause I am looking forward to it as it's my nation's requirement. This is not to promote any programming language or course. Just I need to know it now. ( if my question does not fit here please where it fit most and be kind enough to move to that forum. I had bitter experience about it)

• speech-recognition

• having a sound database for the way to speak words is necessary. This is what's called TTS engine. Last company i worked for we use to create our own TTS because some language/dialect were not availaible on the market. You need 2 intepret, male and female of generic voice tone and there is a specific list of words they need to read and you record them. tis will generate mostly all possible sound you need. Most language will required between 600,000 and 700,000 words to be recorded. –  Franck Commented Feb 12, 2015 at 13:10
• @Franck, Thanks. Our community is ready to contribute those sounds, no matter whatever amount of times that require. By the way what technology was your last company using for developing the system? –  Monolord's Knight Commented Feb 12, 2015 at 13:35
• There was no coding involve as far as i know for recognition and database building. We had a professional recording room where the women as been recording these 700k words for about a 10 month to 1 year. In the end to do speech recognition we had a TTS server that's quite expensive. If i recall it's in the 6 digits and it's a whole OS by itself. We were calling it with C#, asp classic, asp.net, VXML, and some hardware phone system. –  Franck Commented Feb 12, 2015 at 13:49
• 1 A TTS (Text to speech) engine is the reverse of speech recognition isn't it? TTS enables the computer to produce speech rather than understand it. –  Matthew Lock Commented Feb 12, 2015 at 23:01
• I don't know how they fare for speech recognition, but Python and Java have NLP toolkits. Speed-wise, go with C++ –  Agi Hammerthief Commented Feb 13, 2015 at 16:24

2 Answers 2

Adding support for a new language is pretty straightforward, you actually just need to follow the documentation and you can get to the point. You also need to have a knowledge of the scripting language which will help you to cut manual work on some steps. Unix command line experience is a big plus, though you can work on Windows too.

1) Read Introduction to become familiar with concepts of speech recognition - features, acoustic models, language models, etc.

2) Try CMUSphinx with US English model to understand how things work. Try to train with sample US English AN4 database following acoustic model training tutorial .

3) Read about your language in Wikipedia.

4) Collect a set of transcribed recordings for your language - podcasts, radio shows, audiobooks. You can also record some initial amount yourself. You need about 20 hours of transcribed data to start, 100 hours to create a good model.

5) Based on the data you collected, create a list of words and a phonetic dictionary. Most phonetic dictionaries could be created with a simple rules with a small script in your favorite scripting language like Python. See Generating a dictionary for details.

6) Segment the audio to short sentences manually or with sphinx4 aligner, create a database with required files as described in training tutorial.

7) Integrate new model into your application and design a data collection to improve your model.

If you have questions, feel free to ask on CMU Sphinx / Forums .

See if any existing speech recognition systems have a way to add a custom language. That will save you years of effort. Even then, collecting a big database of your language's words and grammar will be immense.

Here's some leads to get you started:

• http://cmusphinx.sourceforge.net/wiki/tutorialdict
• http://kaldi.sourceforge.net/about.html

If not then you probably will be using C/C++ in order to process the incoming audio fast enough. You can learn more about the principles of speech recognition here: http://en.wikipedia.org/wiki/Speech_recognition

• thanks for the info. Please let me know any update you receive. I'm trying those out. –  Monolord's Knight Commented Feb 12, 2015 at 13:37
• 1 Actually a single person can do it in about a month, only passion is required. It is not that complex and C/C++ is not needed. –  Nikolay Shmyrev Commented Mar 15, 2015 at 23:51
• @NikolayShmyrev awesome! –  Matthew Lock Commented Mar 16, 2015 at 0:02

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The best dictation software in 2024

These speech-to-text apps will save you time without sacrificing accuracy..

The early days of dictation software were like your friend that mishears lyrics: lots of enthusiasm but little accuracy. Now, AI is out of Pandora's box, both in the news and in the apps we use, and dictation apps are getting better and better because of it. It's still not 100% perfect, but you'll definitely feel more in control when using your voice to type.

I took to the internet to find the best speech-to-text software out there right now, and after monologuing at length in front of dozens of dictation apps, these are my picks for the best.

The best dictation software

What is dictation software.

If this isn't what you're looking for, here's what else is out there:

AI assistants, such as Apple's Siri, Amazon's Alexa, and Microsoft's Cortana, can help you interact with each of these ecosystems to send texts, buy products, or schedule events on your calendar.

Transcription services that use a combination of dictation software, AI, and human proofreaders can achieve above 99% accuracy.

What makes a great dictation app?

How we evaluate and test apps.

Dictation software comes in different shapes and sizes. Some are integrated in products you already use. Others are separate apps that offer a range of extra features. While each can vary in look and feel, here's what I looked for to find the best:

High accuracy. Staying true to what you're saying is the most important feature here. The lowest score on this list is at 92% accuracy.

Ease of use. This isn't a high hurdle, as most options are basic enough that anyone can figure them out in seconds.

Availability of voice commands. These let you add "instructions" while you're dictating, such as adding punctuation, starting a new paragraph, or more complex commands like capitalizing all the words in a sentence.

Availability of the languages supported. Most of the picks here support a decent (or impressive) number of languages.

Versatility. I paid attention to how well the software could adapt to different circumstances, apps, and systems.

I tested these apps by reading a 200-word script containing numbers, compound words, and a few tricky terms. I read the script three times for each app: the accuracy scores are an average of all attempts. Finally, I used the voice commands to delete and format text and to control the app's features where available.

What about AI?

Also, since this isn't a hot AI software category, these apps may prefer to focus on their core offering and product quality instead, not ride the trendy wave by slapping "AI-powered" on every web page.

Tips for using voice recognition software

Though dictation software is pretty good at recognizing different voices, it's not perfect. Here are some tips to make it work as best as possible.

Speak naturally (with caveats). Dictation apps learn your voice and speech patterns over time. And if you're going to spend any time with them, you want to be comfortable. Speak naturally. If you're not getting 90% accuracy initially, try enunciating more.  

Punctuate. When you dictate, you have to say each period, comma, question mark, and so forth. The software isn't always smart enough to figure it out on its own.

Learn a few commands . Take the time to learn a few simple commands, such as "new line" to enter a line break. There are different commands for composing, editing, and operating your device. Commands may differ from app to app, so learn the ones that apply to the tool you choose.

Know your limits. Especially on mobile devices, some tools have a time limit for how long they can listen—sometimes for as little as 10 seconds. Glance at the screen from time to time to make sure you haven't blown past the mark. 

Practice. It takes time to adjust to voice recognition software, but it gets easier the more you practice. Some of the more sophisticated apps invite you to train by reading passages or doing other short drills. Don't shy away from tutorials, help menus, and on-screen cheat sheets.

The best dictation software at a glance

Best free dictation software for Apple devices

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Look no further than your Mac, iPhone, or iPad for one of the best dictation tools. Apple's built-in dictation feature, powered by Siri (I wouldn't be surprised if the two merged one day), ships as part of Apple's desktop and mobile operating systems. On iOS devices, you use it by pressing the microphone icon on the stock keyboard. On your desktop, you turn it on by going to System Preferences > Keyboard > Dictation , and then use a keyboard shortcut to activate it in your app.

Apple Dictation price: Included with macOS, iOS, iPadOS, and Apple Watch.

Apple Dictation accuracy: 96%. I tested this on an iPhone SE 3rd Gen using the dictation feature on the keyboard.

Best free dictation software for Windows

Windows 11 speech recognition (windows).

Windows 11 Speech Recognition (also known as Voice Typing) is a strong dictation tool, both for writing documents and controlling your Windows PC. Since it's part of your system, you can use it in any app you have installed.

To start, first, check that online speech recognition is on by going to Settings > Time and Language > Speech . To begin dictating, open an app, and on your keyboard, press the Windows logo key + H. A microphone icon and gray box will appear at the top of your screen. Make sure your cursor is in the space where you want to dictate.

When it's ready for your dictation, it will say Listening . You have about 10 seconds to start talking before the microphone turns off. If that happens, just click it again and wait for Listening to pop up. To stop the dictation, click the microphone icon again or say "stop talking."  

As I dictated into a Word document, the gray box reminded me to hang on, we need a moment to catch up . If you're speaking too fast, you'll also notice your transcribed words aren't keeping up. This never posed an issue with accuracy, but it's a nice reminder to keep it slow and steady. 

While you can use this tool anywhere inside your computer, if you're a Microsoft 365 subscriber, you'll be able to use the dictation features there too. The best app to use it on is, of course, Microsoft Word: it even offers file transcription, so you can upload a WAV or MP3 file and turn it into text. The engine is the same, provided by Microsoft Speech Services.

Windows 11 Speech Recognition price: Included with Windows 11. Also available as part of the Microsoft 365 subscription.

Windows 11 Speech Recognition accuracy: 95%. I tested it in Windows 11 while using Microsoft Word. 

Best customizable dictation software

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In 1990, Dragon Dictate emerged as the first dictation software. Over three decades later, we have Dragon by Nuance, a leader in the industry and a distant cousin of that first iteration. With a variety of software packages and mobile apps for different use cases (e.g., legal, medical, law enforcement), Dragon can handle specialized industry vocabulary, and it comes with excellent features, such as the ability to transcribe text from an audio file you upload. 

For this test, I used Dragon Anywhere, Nuance's mobile app, as it's the only version—among otherwise expensive packages—available with a free trial. It includes lots of features not found in the others, like Words, which lets you add words that would be difficult to recognize and spell out. For example, in the script, the word "Litmus'" (with the possessive) gave every app trouble. To avoid this, I added it to Words, trained it a few times with my voice, and was then able to transcribe it accurately.

It also provides shortcuts. If you want to shorten your entire address to one word, go to Auto-Text , give it a name ("address"), and type in your address: 1000 Eichhorn St., Davenport, IA 52722, and hit Save . The next time you dictate and say "address," you'll get the entire thing. Press the comment bubble icon to see text commands while you're dictating, or say "What can I say?" and the command menu pops up. 

Once you complete a dictation, you can email, share (e.g., Google Drive, Dropbox), open in Word, or save to Evernote. You can perform these actions manually or by voice command (e.g., "save to Evernote.") Once you name it, it automatically saves in Documents for later review or sharing. 

Accuracy is good and improves with use, showing that you can definitely train your dragon. It's a great choice if you're serious about dictation and plan to use it every day, but may be a bit too much if you're just using it occasionally.

Dragon by Nuance price: $15/month for Dragon Anywhere (iOS and Android); from $200 to $500 for desktop packages

Dragon by Nuance accuracy: 97%. Tested it in the Dragon Anywhere iOS app.

Best free mobile dictation software

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Back to the topic: it has an excellent dictation feature. To start, press the microphone icon on the top-right of the keyboard. An overlay appears on the screen, filling itself with the words you're saying. It's very quick and accurate, which will feel great for fast-talkers but probably intimidating for the more thoughtful among us. If you stop talking for a few seconds, the overlay disappears, and Gboard pastes what it heard into the app you're using. When this happens, tap the microphone icon again to continue talking.

Wherever you can open a keyboard while using your phone, you can have Gboard supporting you there. You can write emails or notes or use any other app with an input field.

The writer who handled the previous update of this list had been using Gboard for seven years, so it had plenty of training data to adapt to his particular enunciation, landing the accuracy at an amazing 98%. I haven't used it much before, so the best I had was 92% overall. It's still a great score. More than that, it's proof of how dictation apps improve the more you use them.

Gboard price : Free

Gboard accuracy: 92%. With training, it can go up to 98%. I tested it using the iOS app while writing a new email.

Best dictation software for typing in Google Docs

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Just like Microsoft offers dictation in their Office products, Google does the same for their Workspace suite. The best place to use the voice typing feature is in Google Docs, but you can also dictate speaker notes in Google Slides as a way to prepare for your presentation.

To get started, make sure you're using Chrome and have a Google Docs file open. Go to Tools > Voice typing , and press the microphone icon to start. As you talk, the text will jitter into existence in the document.

You can change the language in the dropdown on top of the microphone icon. If you need help, hover over that icon, and click the ? on the bottom-right. That will show everything from turning on the mic, the voice commands for dictation, and moving around the document.

It's unclear whether Google's voice typing here is connected to the same engine in Gboard. I wasn't able to confirm whether the training data for the mobile keyboard and this tool are connected in any way. Still, the engines feel very similar and turned out the same accuracy at 92%. If you start using it more often, it may adapt to your particular enunciation and be more accurate in the long run.

Google Docs voice typing price : Free

Google Docs voice typing accuracy: 92%. Tested in a new Google Docs file in Chrome.

Best dictation software for collaboration

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It's not as robust in terms of dictation as others on the list, but it compensates with its versatility. It's a meeting assistant, first and foremost, ready to hop on your meetings and transcribe everything it hears. This is great to keep track of what's happening there, making the text available for sharing by generating a link or in the corresponding team workspace.

The reason why it's the best for collaboration is that others can highlight parts of the transcript and leave their comments. It also separates multiple speakers, in case you're recording a conversation, so that's an extra headache-saver if you use dictation software for interviewing people.

When you open the app and click the Record button on the top-right, you can use it as a traditional dictation app. It doesn't support voice commands, but it has decent intuition as to where the commas and periods should go based on the intonation and rhythm of your voice. Once you're done talking, Otter will start processing what you said, extract keywords, and generate action items and notes from the content of the transcription.

If you're going for long recording stretches where you talk about multiple topics, there's an AI chat option, where you can ask Otter questions about the transcript. This is great to summarize the entire talk, extract insights, and get a different angle on everything you said.

Otter price: Free plan available for 300 minutes / month. Pro plan starts at $16.99, adding more collaboration features and monthly minutes.

Otter accuracy: 93% accuracy. I tested it in the web app on my computer.

Otter supported languages: Only American and British English for now.

Is voice dictation for you?

Dictation software isn't for everyone. It will likely take practice learning to "write" out loud because it will feel unnatural. But once you get comfortable with it, you'll be able to write from anywhere on any device without the need for a keyboard. 

And by using any of the apps I listed here, you can feel confident that most of what you dictate will be accurately captured on the screen. 

Related reading:

This article was originally published in April 2016 and has also had contributions from Emily Esposito, Jill Duffy, and Chris Hawkins. The most recent update was in November 2023.

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Miguel Rebelo

Miguel Rebelo is a freelance writer based in London, UK. He loves technology, video games, and huge forests. Track him down at mirebelo.com.

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Best text-to-speech software of 2024

Boosting accessibility and productivity

• Best overall
• Best realism
• Best for developers
• Best for podcasting
• How we test

The best text-to-speech software makes it simple and easy to convert text to voice for accessibility or for productivity applications.

1. Best overall 2. Best realism 3. Best for developers 4. Best for podcasting 5. Best for developers 6. FAQs 7. How we test

Finding the best text-to-speech software is key for anyone looking to transform written text into spoken words, whether for accessibility purposes, productivity enhancement, or creative applications like voice-overs in videos. 

Text-to-speech (TTS) technology relies on sophisticated algorithms to model natural language to bring written words to life, making it easier to catch typos or nuances in written content when it's read aloud. So, unlike the best speech-to-text apps and best dictation software , which focus on converting spoken words into text, TTS software specializes in the reverse process: turning text documents into audio. This technology is not only efficient but also comes with a variety of tools and features. For those creating content for platforms like YouTube , the ability to download audio files is a particularly valuable feature of the best text-to-speech software.

While some standard office programs like Microsoft Word and Google Docs offer basic TTS tools, they often lack the comprehensive functionalities found in dedicated TTS software. These basic tools may provide decent accuracy and basic options like different accents and languages, but they fall short in delivering the full spectrum of capabilities available in specialized TTS software.

To help you find the best text-to-speech software for your specific needs, TechRadar Pro has rigorously tested various software options, evaluating them based on user experience, performance, output quality, and pricing. This includes examining the best free text-to-speech software as well, since many free options are perfect for most users. We've brought together our picks below to help you choose the most suitable tool for your specific needs, whether for personal use, professional projects, or accessibility requirements.

The best text-to-speech software of 2024 in full:

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Below you'll find full write-ups for each of the entries on our best text-to-speech software list. We've tested each one extensively, so you can be sure that our recommendations can be trusted.

The best text-to-speech software overall

1. NaturalReader

Our expert review:

Reasons to buy

Reasons to avoid.

If you’re looking for a cloud-based speech synthesis application, you should definitely check out NaturalReader. Aimed more at personal use, the solution allows you to convert written text such as Word and PDF documents, ebooks and web pages into human-like speech.  

Because the software is underpinned by cloud technology, you’re able to access it from wherever you go via a smartphone, tablet or computer. And just like Capti Voice, you can upload documents from cloud storage lockers such as Google Drive, Dropbox and OneDrive.  

Currently, you can access 56 natural-sounding voices in nine different languages, including American English, British English, French, Spanish, German, Swedish, Italian, Portuguese and Dutch. The software supports PDF, TXT, DOC(X), ODT, PNG, JPG, plus non-DRM EPUB files and much more, along with MP3 audio streams. 

There are three different products: online, software, and commercial. Both the online and software products have a free tier.

Read our full NaturalReader review .

• ^ Back to the top

The best text-to-speech software for realistic voices

Specializing in voice synthesis technology, Murf uses AI to generate realistic voiceovers for a range of uses, from e-learning to corporate presentations. 

Murf comes with a comprehensive suite of AI tools that are easy to use and straightforward to locate and access. There's even a Voice Changer feature that allows you to record something before it is transformed into an AI-generated voice- perfect if you don't think you have the right tone or accent for a piece of audio content but would rather not enlist the help of a voice actor. Other features include Voice Editing, Time Syncing, and a Grammar Assistant.

The solution comes with three pricing plans to choose from: Basic, Pro and Enterprise. The latter of these options may be pricey but some with added collaboration and account management features that larger companies may need access to. The Basic plan starts at around $19 / £17 / AU$28 per month but if you set up a yearly plan that will drop to around $13 / £12 / AU$20 per month. You can also try the service out for free for up to 10 minutes, without downloads.

The best text-to-speech software for developers

3. Amazon Polly

Alexa isn’t the only artificial intelligence tool created by tech giant Amazon as it also offers an intelligent text-to-speech system called Amazon Polly. Employing advanced deep learning techniques, the software turns text into lifelike speech. Developers can use the software to create speech-enabled products and apps. 

It sports an API that lets you easily integrate speech synthesis capabilities into ebooks, articles and other media. What’s great is that Polly is so easy to use. To get text converted into speech, you just have to send it through the API, and it’ll send an audio stream straight back to your application. 

You can also store audio streams as MP3, Vorbis and PCM file formats, and there’s support for a range of international languages and dialects. These include British English, American English, Australian English, French, German, Italian, Spanish, Dutch, Danish and Russian. 

Polly is available as an API on its own, as well as a feature of the AWS Management Console and command-line interface. In terms of pricing, you’re charged based on the number of text characters you convert into speech. This is charged at approximately $16 per1 million characters , but there is a free tier for the first year.

The best text-to-speech software for podcasting

In terms of its library of voice options, it's hard to beat Play.ht as one of the best text-to-speech software tools. With almost 600 AI-generated voices available in over 60 languages, it's likely you'll be able to find a voice to suit your needs. 

Although the platform isn't the easiest to use, there is a detailed video tutorial to help users if they encounter any difficulties. All the usual features are available, including Voice Generation and Audio Analytics. 

In terms of pricing, Play.ht comes with four plans: Personal, Professional, Growth, and Business. These range widely in price, but it depends if you need things like commercial rights and affects the number of words you can generate each month. 

The best text-to-speech software for Mac and iOS

5. Voice Dream Reader

There are also plenty of great text-to-speech applications available for mobile devices, and Voice Dream Reader is an excellent example. It can convert documents, web articles and ebooks into natural-sounding speech. 

The app comes with 186 built-in voices across 30 languages, including English, Arabic, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, Finnish, French, German, Greek, Hebrew, Hungarian, Italian, Japanese and Korean. 

You can get the software to read a list of articles while you drive, work or exercise, and there are auto-scrolling, full-screen and distraction-free modes to help you focus. Voice Dream Reader can be used with cloud solutions like Dropbox, Google Drive, iCloud Drive, Pocket, Instapaper and Evernote. 

The best text-to-speech software: FAQs

What is the best text-to-speech software for youtube.

If you're looking for the best text-to-speech software for YouTube videos or other social media platforms, you need a tool that lets you extract the audio file once your text document has been processed. Thankfully, that's most of them. So, the real trick is to select a TTS app that features a bountiful choice of natural-sounding voices that match the personality of your channel. 

What’s the difference between web TTS services and TTS software?

Web TTS services are hosted on a company or developer website. You’ll only be able to access the service if the service remains available at the whim of a provider or isn’t facing an outage.

TTS software refers to downloadable desktop applications that typically won’t rely on connection to a server, meaning that so long as you preserve the installer, you should be able to use the software long after it stops being provided. 

Do I need a text-to-speech subscription?

Subscriptions are by far the most common pricing model for top text-to-speech software. By offering subscription models for, companies and developers benefit from a more sustainable revenue stream than they do from simply offering a one-time purchase model. Subscription models are also attractive to text-to-speech software providers as they tend to be more effective at defeating piracy.

Free software options are very rarely absolutely free. In some cases, individual voices may be priced and sold individually once the application has been installed or an account has been created on the web service.

How can I incorporate text-to-speech as part of my business tech stack?

Some of the text-to-speech software that we’ve chosen come with business plans, offering features such as additional usage allowances and the ability to have a shared workspace for documents. Other than that, services such as Amazon Polly are available as an API for more direct integration with business workflows.

Small businesses may find consumer-level subscription plans for text-to-speech software to be adequate, but it’s worth mentioning that only business plans usually come with the universal right to use any files or audio created for commercial use.

How to choose the best text-to-speech software

When deciding which text-to-speech software is best for you, it depends on a number of factors and preferences. For example, whether you’re happy to join the ecosystem of big companies like Amazon in exchange for quality assurance, if you prefer realistic voices, and how much budget you’re playing with. It’s worth noting that the paid services we recommend, while reliable, are often subscription services, with software hosted via websites, rather than one-time purchase desktop apps. 

Also, remember that the latest versions of Microsoft Word and Google Docs feature basic text-to-speech as standard, as well as most popular browsers. So, if you have access to that software and all you’re looking for is a quick fix, that may suit your needs well enough. 

How we test the best text-to-speech software

We test for various use cases, including suitability for use with accessibility issues, such as visual impairment, and for multi-tasking. Both of these require easy access and near instantaneous processing. Where possible, we look for integration across the entirety of an operating system , and for fair usage allowances across free and paid subscription models.

At a minimum, we expect an intuitive interface and intuitive software. We like bells and whistles such as realistic voices, but we also appreciate that there is a place for products that simply get the job done. Here, the question that we ask can be as simple as “does this piece of software do what it's expected to do when asked?”

Read more on how we test, rate, and review products on TechRadar .

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Updated : 6 June 2024 Contributor : Jim Holdsworth

Natural language processing (NLP) is a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language. 

NLP enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics—the rule-based modeling of human language—together with statistical modeling,  machine learning (ML)  and deep learning. 

NLP research has enabled the era of generative AI, from the communication skills of large language models (LLMs) to the ability of image generation models to understand requests. NLP is already part of everyday life for many, powering search engines, prompting chatbots for customer service with spoken commands, voice-operated GPS systems and digital assistants on smartphones. NLP also plays a growing role in enterprise solutions that help streamline and automate business operations, increase employee productivity and simplify mission-critical business processes.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

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A natural language processing system can work rapidly and efficiently: after NLP models are properly trained, it can take on administrative tasks, freeing staff for more productive work. Benefits can include:

Faster insight discovery : Organizations can find hidden patterns, trends and relationships between different pieces of content. Text data retrieval supports deeper insights and analysis, enabling better-informed decision-making and surfacing new business ideas.

Greater budget savings : With the massive volume of unstructured text data available, NLP can be used to automate the gathering, processing and organization of information with less manual effort.

Quick access to corporate data : An enterprise can build a knowledge base of organizational information to be efficiently accessed with AI search. For sales representatives, NLP can help quickly return relevant information, to improve customer service and help close sales.

NLP models are not perfect and probably never will be, just as human speech is prone to error. Risks might include:

Biased training :  As with any AI function, biased data used in training will skew the answers. The more diverse the users of an NLP function, the more significant this risk becomes, such as in government services, healthcare and HR interactions. Training datasets scraped from the web, for example, are prone to bias.

Misinterpretation : As in programming, there is a risk of garbage in, garbage out (GIGO). NLP solutions might become confused if spoken input is in an obscure dialect, mumbled, too full of slang, homonyms, incorrect grammar, idioms, fragments, mispronunciations, contractions or recorded with too much background noise.

New vocabulary: New words are continually being invented or imported. The conventions of grammar can evolve or be intentionally broken. In these cases, NLP can either make a best guess or admit it’s unsure—and either way, this creates a complication.

Tone of voice : When people speak, their verbal delivery or even body language can give an entirely different meaning than the words alone. Exaggeration for effect, stressing words for importance or sarcasm can be confused by NLP, making the semantic analysis more difficult and less reliable.

Human language is filled with many ambiguities that make it difficult for programmers to write software that accurately determines the intended meaning of text or voice data. Human language might take years for humans to learn—and many never stop learning. But then programmers must teach natural language-driven applications to recognize and understand irregularities so their applications can be accurate and useful.

NLP combines the power of computational linguistics together with machine learning algorithms and deep learning. Computational linguistics is a discipline of linguistics that uses data science to analyze language and speech. It includes two main types of analysis: syntactical analysis and semantical analysis. Syntactical analysis determines the meaning of a word, phrase or sentence by parsing the syntax of the words and applying preprogrammed rules of grammar. Semantical analysis uses the syntactic output to draw meaning from the words and interpret their meaning within the sentence structure. 

The parsing of words can take one of two forms. Dependency parsing looks at the relationships between words, such as identifying nouns and verbs, while constituency parsing then builds a parse tree (or syntax tree): a rooted and ordered representation of the syntactic structure of the sentence or string of words. The resulting parse trees underly the functions of language translators and speech recognition. Ideally, this analysis makes the output—either text or speech—understandable to both NLP models and people.

Self-supervised learning (SSL) in particular is useful for supporting NLP because NLP requires large amounts of labeled data to train state-of-the-art artificial intelligence (AI) models . Because these labeled datasets require time-consuming annotation—a process involving manual labeling by humans—gathering sufficient data can be prohibitively difficult. Self-supervised approaches can be more time-effective and cost-effective, as they replace some or all manually labeled training data. Three different approaches to NLP include:

Rules-based NLP : The earliest NLP applications were simple if-then decision trees, requiring preprogrammed rules. They are only able to provide answers in response to specific prompts, such as the original version of Moviefone. Because there is no machine learning or AI capability in rules-based NLP, this function is highly limited and not scalable.

Statistical NLP : Developed later, statistical NLP automatically extracts, classifies and labels elements of text and voice data, and then assigns a statistical likelihood to each possible meaning of those elements. This relies on machine learning, enabling a sophisticated breakdown of linguistics such as part-of-speech tagging. Statistical NLP introduced the essential technique of mapping language elements—such as words and grammatical rules—to a vector representation so that language can be modeled by using mathematical (statistical) methods, including regression or Markov models. This informed early NLP developments such as spellcheckers and T9 texting (Text on 9 keys, to be used on Touch-Tone telephones).

Deep learning NLP : Recently, deep learning models have become the dominant mode of NLP, by using huge volumes of raw, unstructured data—both text and voice—to become ever more accurate. Deep learning can be viewed as a further evolution of statistical NLP, with the difference that it uses neural network models. There are several subcategories of models:

• Sequence-to-Sequence (seq2seq) models : Based on recurrent neural networks (RNN) , they have mostly been used for machine translation by converting a phrase from one domain (such as the German language) into the phrase of another domain (such as English).
• Transformer models : They use tokenization of language (the position of each token—words or subwords) and self-attention (capturing dependencies and relationships) to calculate the relation of different language parts to one another. Transformer models can be efficiently trained by using self-supervised learning on massive text databases. A landmark in transformer models was Google’s bidirectional encoder representations from transformers (BERT), which became and remains the basis of how Google’s search engine works.
• Autoregressive models : This type of transformer model is trained specifically to predict the next word in a sequence, which represents a huge leap forward in the ability to generate text. Examples of autoregressive LLMs include GPT, Llama , Claude and the open-source Mistral.
• Foundation models : Prebuilt and curated foundation models can speed the launching of an NLP effort and boost trust in its operation. For example, the IBM Granite™ foundation models are widely applicable across industries. They support NLP tasks including content generation and insight extraction. Additionally, they facilitate retrieval-augmented generation, a framework for improving the quality of response by linking the model to external sources of knowledge. The models also perform named entity recognition which involves identifying and extracting key information in a text.

For a deeper dive into the nuances between multiple technologies and their learning approaches, see “ AI versus. machine learning versus deep learning versus neural networks: What’s the difference? ”

Several NLP tasks typically help process human text and voice data in ways that help the computer make sense of what it’s ingesting. Some of these tasks include:

Linguistic tasks

• Coreference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (such as, “she” = “Mary”). But it can also identify a metaphor or an idiom in the text (such as an instance in which “bear” isn’t an animal, but a large and hairy person).
• Named entity recognition  ( NER ) identifies words or phrases as useful entities. NER identifies “London” as a location or “Maria” as a person's name.
• Part-of-speech tagging , also called grammatical tagging, is the process of determining which part of speech a word or piece of text is, based on its use and context. For example, part-of-speech identifies “make” as a verb in “I can make a paper plane,” and as a noun in “What make of car do you own?”
• Word sense disambiguation is the selection of a word meaning for a word with multiple possible meanings. This uses a process of semantic analysis to examine the word in context. For example, word sense disambiguation helps distinguish the meaning of the verb “make” in “make the grade” (to achieve) versus “make a bet” (to place). Sorting out “I will be merry when I marry Mary” requires a sophisticated NLP system.

User-supporting tasks

• Speech recognition , also known as speech-to-text , is the task of reliably converting voice data into text data. Speech recognition is part of any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people speak—quickly, running words together, with varying emphasis and intonation.
• Natural language generation (NLG) might be described as the opposite of speech recognition or speech-to-text: NLG is the task of putting structured information into conversational human language. Without NLG, computers would have little chance of passing the Turing test , where a computer tries to mimic a human conversation. Conversational agents such as Amazon’s Alexa and Apple’s Siri are already doing this well and assisting customers in real time.
• Natural language understanding (NLU) is a subset of NLP that focuses on analyzing the meaning behind sentences. NLU enables software to find similar meanings in different sentences or to process words that have different meanings.
• Sentiment analysis  attempts to extract subjective qualities —attitudes, emotions, sarcasm, confusion or suspicion—from text. This is often used for routing communications to the system or the person most likely to make the next response.

See the blog post “ NLP vs. NLU vs. NLG: the differences between three natural language processing concepts ” for a deeper look into how these concepts relate.

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Organizations can use NLP to process communications that include email, SMS, audio, video, newsfeeds and social media. NLP is the driving force behind AI in many modern real-world applications. Here are a few examples:

• Customer assistance : Enterprises can deploy chatbots or virtual assistants to quickly respond to custom questions and requests. When questions become too difficult for the chatbot or virtual assistant, the NLP system moves the customer over to a human customer service agent. Virtual agents such as IBM watsonx™ Assistant , Apple’s Siri and Amazon’s Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate actions or helpful comments. Chatbots respond to typed text entries. The best chatbots also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to questions—anticipated or not—with relevant and helpful answers in their own words. These automations help reduce costs, save agents from spending time on redundant queries and improve customer satisfaction. Not all chatbots are powered by AI, but state-of-the-art chatbots increasingly use conversational AI techniques, including NLP, to understand user questions and automate responses to them.
• FAQ : Not everyone wants to read to discover an answer. Fortunately, NLP can enhance FAQs: When the user asks a question, the NLP function looks for the best match among the available answers and brings that to the user’s screen. Many customer questions are of the who/what/when/where variety, so this function can save staff from having to repeatedly answer the same routine questions.
• Grammar correction : The rules of grammar can be applied within word processing or other programs, where the NLP function is trained to spot incorrect grammar and suggest corrected wordings.
• Machine translation:  Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words from one language with words of another. Effective translation accurately captures the meaning and tone of the input language and translates it to text with the same meaning and desired impact in the output language. Machine translation tools are becoming more accurate. One way to test a machine translation tool is to translate text from one language and then back to the original. An oft-cited, classic example: Translating “ The spirit is willing, but the flesh is weak” from English to Russian and back again once yielded, “ The vodka is good, but the meat is rotten .” Recently, a closer result was “ The spirit desires, but the flesh is weak. ” Google translate can now take English to Russian to English and return the original, “ The spirit is willing, but the flesh is weak."        
• Redaction of personally identifiable information (PII) : NLP models can be trained to quickly locate personal information in documents that might identify individuals. Industries that handle large volumes of sensitive information—financial, healthcare, insurance and legal firms—can quickly create versions with the PII removed.
• Sentiment analysis : After being trained on industry-specific or business-specific language, an NLP model can quickly scan incoming text for keywords and phrases to gauge a customer’s mood in real-time as positive, neutral or negative. The mood of the incoming communication can help determine how it will be handled. And the incoming communication doesn’t have to be live: NLP can also be used to analyze customer feedback or call center recordings. Another option is an NLP API that can enable after-the-fact text analytics. NLP can uncover actionable data insights from social media posts, responses or reviews to extract attitudes and emotions in response to products, promotions and events. Information companies can use sentiment analysis in product designs, advertising campaigns and more.
• Spam detection:  Many people might not think of spam detection as an NLP solution, but the best spam detection technologies use NLP’s text classification capabilities to scan emails for language indicating spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency or misspelled company names.
• Text generation : NLP helps put the “generative” into generative AI. NLP enables computers to generate text or speech that is natural-sounding and realistic enough to be mistaken for human communication. The generated language might be used to create initial drafts of blogs, computer code, letters, memos or tweets. With an enterprise-grade system, the quality of generated language might be sufficient to be used in real time for autocomplete functions, chatbots or virtual assistants. Advancements in NLP are powering the reasoning engine behind generative AI systems, driving further opportunities. Microsoft® Copilot is an AI assistant designed to boost employee productivity and creativity across day-to-day tasks and is already at work in tools used every day.  
• Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, for busy readers who don't have time to read the full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.
• Finance : In financial dealings, nanoseconds might make the difference between success and failure when accessing data, or making trades or deals. NLP can speed the mining of information from financial statements, annual and regulatory reports, news releases or even social media.
• Healthcare : New medical insights and breakthroughs can arrive faster than many healthcare professionals can keep up. NLP and AI-based tools can help speed the analysis of health records and medical research papers, making better-informed medical decisions possible, or assisting in the detection or even prevention of medical conditions.
• Insurance : NLP can analyze claims to look for patterns that can identify areas of concern and find inefficiencies in claims processing—leading to greater optimization of processing and employee efforts.
• Legal : Almost any legal case might require reviewing mounds of paperwork, background information and legal precedent. NLP can help automate legal discovery, assisting in the organization of information, speeding review and helping ensure that all relevant details are captured for consideration.

Python and the Natural Language Toolkit (NLTK)

The Python programing language provides a wide range of tools and libraries for performing specific NLP tasks. Many of these NLP tools are in the Natural Language Toolkit , or NLTK, an open-source collection of libraries, programs and education resources for building NLP programs.

The NLTK includes libraries for many NLP tasks and subtasks, such as sentence parsing , word segmentation , stemming and lemmatization (methods of trimming words down to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the computer better understand the text). It also includes libraries for implementing capabilities such as semantic reasoning: the ability to reach logical conclusions based on facts extracted from text. Using NLTK, organizations can see the product of part-of-speech tagging. Tagging words might not seem to be complicated, but since words can have different meanings depending on where they are used, the process is complicated.

Generative AI platforms

Organizations can infuse the power of NLP into their digital solutions by leveraging user-friendly generative AI platforms such as IBM Watson NLP Library for Embed , a containerized library designed to empower IBM partners with greater AI capabilities. Developers can access and integrate it into their apps in their environment of their choice to create enterprise-ready solutions with robust AI models, extensive language coverage and scalable container orchestration.

More options include IBM ® watsonx.ai™ AI studio , which enables multiple options to craft model configurations that support a range of NLP tasks including question answering, content generation and summarization, text classification and extraction. Integrations can also enable more NLP capabilities. For example, with watsonx and Hugging Face AI builders can use pretrained models to support a range of NLP tasks.

Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications.

Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility.

Learn the fundamental concepts for AI and generative AI, including prompt engineering, large language models and the best open source projects.

Learn about different NLP use cases in this NLP explainer.

Visit the IBM Developer's website to access blogs, articles, newsletters and more. Become an IBM partner and infuse IBM Watson embeddable AI in your commercial solutions today. Use IBM Watson NLP Library for Embed in your solutions.

Watch IBM Data and AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries.

Learn about the Natural Language Understanding API with example requests and links to additional resources.

IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data.

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iOS 18 makes iPhone more personal, capable, and intelligent than ever

New Levels of Customization and Capability

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Apple Intelligence Transforms the iPhone Experience

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Text of this article

June 10, 2024

PRESS RELEASE

The release introduces all-new customization options, the biggest-ever redesign of Photos, powerful updates for staying connected, and Apple Intelligence, the personal intelligence system

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When messaging contacts who do not have an Apple device, the Messages app now supports RCS for richer media and more reliable group messaging compared to SMS and MMS.

iOS 18 introduces Messages via satellite for the times when cellular and Wi-Fi connections aren’t available. Powered by the same groundbreaking technology as existing iPhone satellite capabilities, Messages via satellite automatically prompts users to connect to their nearest satellite right from the Messages app to send and receive texts, emoji, and Tapbacks over iMessage and SMS. 3 With Dynamic Island, users always know when they are connected to a satellite. Because iMessage was built to protect user privacy, iMessages sent via satellite are end-to-end encrypted.

Later this year, Mail will introduce new ways for users to manage their inbox and stay up to date. On-device categorization organizes and sorts incoming email into Primary for personal and time-sensitive emails, Transactions for confirmations and receipts, Updates for news and social notifications, and Promotions for marketing emails and coupons. Mail also features a new digest view that pulls together all of the relevant emails from a business, allowing users to quickly scan for what’s important in the moment.

Safari, the world’s fastest browser, 4 now offers an even easier way to discover information on the web with Highlights and a redesigned Reader experience. Using machine learning, Safari can surface key information about a webpage. For example, users can review a summary to get the gist of an article; quickly see the location of a restaurant, hotel, or landmark; or listen to an artist’s track right from an article about the song or album. Reader has been redesigned to offer even more ways to enjoy articles without distraction, with a summary and table of contents included for longer articles.

Building on the foundation of Keychain, which was first introduced more than 25 years ago, the new Passwords app makes it easy for users to access their passwords, passkeys, Wi-Fi passwords, and verification codes. The app also includes alerts for users regarding common weaknesses, such as passwords that are easily guessed or used multiple times and those that appear in known data leaks.

iOS 18 gives users even more control with tools to manage who can see their apps, how contacts are shared, and how their iPhone connects to accessories.

Locked and hidden apps offer users peace of mind that information they want to keep private, such as app notifications and content, will not inadvertently be seen by others. Users can now lock an app; and for additional privacy, they can hide an app, moving it to a locked, hidden apps folder. When an app is locked or hidden, content like messages or emails inside the app are hidden from search, notifications, and other places across the system.

iOS 18 puts users in control by letting them choose to share only specific contacts with an app. In addition, developers now have a way to seamlessly connect third-party accessories with iPhone without letting an app see all the other devices on a user’s network, keeping a user’s devices private and making pairing seamless.

Deeply integrated into iOS 18 and built with privacy from the ground up, Apple Intelligence unlocks new ways for users to enhance their writing and communicate more effectively. With brand-new systemwide Writing Tools built into iOS 18, users can rewrite, proofread, and summarize text nearly everywhere they write, including Mail, Notes, Pages, and third-party apps.

New image capabilities make communication and self-expression even more fun. With Image Playground, users can create playful images in seconds, choosing from three styles: Animation, Illustration, or Sketch. Image Playground is easy to use, built right into apps like Messages, and also available in a dedicated app.

Memories in Photos lets users create the stories they want to see just by typing a description. Apple Intelligence will pick out the best photos and videos based on the description, craft a storyline with chapters based on themes identified from the photos, and arrange them into a movie with its own narrative arc. In addition, a new Clean Up tool can identify and remove distracting objects in the background of a photo — without accidentally altering the subject.

With the power of Apple Intelligence, Siri takes a major step forward, becoming even more natural, contextually relevant, and personal. Users can type to Siri, and switch between text and voice to communicate with Siri in whatever way feels right for the moment.

With Private Cloud Compute, Apple sets a new standard for privacy in AI, with the ability to flex and scale computational capacity between on-device processing, and larger, server-based models that run on dedicated Apple silicon servers. When requests are routed to Private Cloud Compute, data is not stored or made accessible to Apple and is only used to fulfill the user’s requests, and independent experts can verify this privacy.

Additionally, access to ChatGPT is integrated into Siri and systemwide Writing Tools across Apple’s platforms, allowing users to access its expertise — as well as its image- and document-understanding capabilities — without needing to jump between tools.

Additional features in iOS 18 include: 

Availability

The developer beta of iOS 18 is available through the Apple Developer Program at developer.apple.com starting today, and a public beta will be available through the Apple Beta Software Program next month at beta.apple.com . iOS 18 will be available this fall as a free software update for iPhone Xs and later. Apple Intelligence will be available in beta on iPhone 15 Pro, iPhone 15 Pro Max, and iPad and Mac with M1 and later, with Siri and device language set to U.S. English, as part of iOS 18, iPadOS 18, and macOS Sequoia this fall. For more information, visit apple.com/ios/ios-18-preview and apple.com/apple-intelligence . Features are subject to change. Some features are not available in all regions, all languages, or on all devices. For more information about availability, visit apple.com .

• Messages via satellite will be available in iOS 18 along with Apple’s existing satellite features in the U.S. on iPhone 14 and later.
• Users with an eligible iPhone, iPad, or Mac, and Siri and device language set to English (U.S.) can sign up this fall to access the Apple Intelligence beta.
• SMS availability will depend on carrier. Carrier fees may apply. Users should check with their carrier for details.
• Testing was conducted by Apple in May 2023. See apple.com/safari  for more information.
• The new Apple Pay features are available on cards from participating banks and card providers in certain markets. Subject to eligibility and approval.
• Apple Cash services are provided by Green Dot Bank, Member FDIC, and only available in the U.S. on eligible devices. Learn more about the terms and conditions . To send and receive money with an Apple Cash account, users must be 18 and a U.S. resident, or if under 18, part of an Apple Cash Family account. Tap to Cash transaction limits are subject to change, including lowering limits, at any time during the developer or public betas without notice.
• Ticket enhancements in Apple Wallet are available for events from participating ticket issuers.

Press Contacts

Nadine Haija

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Tania Olkhovaya

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Apple Media Helpline

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