speech recognition js

JavaScript Speech Recognition Example (Speech to Text)

With the Web Speech API, we can recognize speech using JavaScript . It is super easy to recognize speech in a browser using JavaScript and then getting the text from the speech to use as user input. We have already covered How to convert Text to Speech in Javascript .

But the support for this API is limited to the Chrome browser only . So if you are viewing this example in some other browser, the live example below might not work.

This tutorial will cover a basic example where we will cover speech to text. We will ask the user to speak something and we will use the SpeechRecognition object to convert the speech into text and then display the text on the screen.

The Web Speech API of Javascript can be used for multiple other use cases. We can provide a list of rules for words or sentences as grammar using the SpeechGrammarList object, which will be used to recognize and validate user input from speech.

For example, consider that you have a webpage on which you show a Quiz, with a question and 4 available options and the user has to select the correct option. In this, we can set the grammar for speech recognition with only the options for the question, hence whatever the user speaks, if it is not one of the 4 options, it will not be recognized.

We can use grammar, to define rules for speech recognition, configuring what our app understands and what it doesn't understand.

JavaScript Speech to Text

In the code example below, we will use the SpeechRecognition object. We haven't used too many properties and are relying on the default values. We have a simple HTML webpage in the example, where we have a button to initiate the speech recognition.

The main JavaScript code which is listening to what user speaks and then converting it to text is this:

In the above code, we have used:

recognition.start() method is used to start the speech recognition.

Once we begin speech recognition, the onstart event handler can be used to inform the user that speech recognition has started and they should speak into the mocrophone.

When the user is done speaking, the onresult event handler will have the result. The SpeechRecognitionEvent results property returns a SpeechRecognitionResultList object. The SpeechRecognitionResultList object contains SpeechRecognitionResult objects. It has a getter so it can be accessed like an array. The first [0] returns the SpeechRecognitionResult at the last position. Each SpeechRecognitionResult object contains SpeechRecognitionAlternative objects that contain individual results. These also have getters so they can be accessed like arrays. The second [0] returns the SpeechRecognitionAlternative at position 0 . We then return the transcript property of the SpeechRecognitionAlternative object.

Same is done for the confidence property to get the accuracy of the result as evaluated by the API.

We have many event handlers, to handle the events surrounding the speech recognition process. One such event is onspeechend , which we have used in our code to call the stop() method of the SpeechRecognition object to stop the recognition process.

Now let's see the running code:

When you will run the code, the browser will ask for permission to use your Microphone , so please click on Allow and then speak anything to see the script in action.

Conclusion:

So in this tutorial we learned how we can use Javascript to write our own small application for converting speech into text and then displaying the text output on screen. We also made the whole process more interactive by using the various event handlers available in the SpeechRecognition interface. In future I will try to cover some simple web application ideas using this feature of Javascript to help you usnderstand where we can use this feature.

If you face any issue running the above script, post in the comment section below. Remember, only Chrome browser supports it .

You may also like:

• JavaScript Window Object
• JavaScript Number Object
• JavaScript Functions
• JavaScript Document Object

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Related posts.

Voice commands and speech synthesis made easy

Artyom.js is an useful wrapper of the speechSynthesis and webkitSpeechRecognition APIs.

Besides, artyom.js also lets you to add voice commands to your website easily, build your own Google Now, Siri or Cortana !

Download .js

Get on bower, installation.

If you don't use any module bundler like browserify, require etc, just include the artyom window script in the head tag of your document and you are ready to go !

The Artyom class would be now available and you can instantiate it:

Note You need to load artyom.js in the head tag to preload the voices in case you want to use the speechSynthesis API. otherwise you can still load it in the end of the body tag.

About Artyom in this Browser

Loading info ....

According to your browser, speech synthesis and speech recognition may be available or not separately, use artyom.speechSupported and artyom.recognizingSupported methods to know it.

These are the available voices of artyom in this browser. See the initialization codes in the initialization area or read the docs.

Our Code Editor

Give artyom some orders in this website Since you're in this website artyom has been enabled. Try using any of the demo commands in the following list to test it !

Voice commands

Before the initialization, we need to add some commands for being processed. Use the artyom.addCommands(commands) method to add commands.

A command is a literal object with some properties. There are 2 types of commands normal and smarts .

A smart command allow you to retrieve a value from a spoken string as a wildcard. Every command can be triggered for any of the identifiers given in the indexes array.

Pro tip You can add commands dinamically while artyom is active. The commands are stored in an array so you can add them whenever you want and they'll be processed.

Start artyom

Now that artyom has commands, these can be processed. Artyom can work in continuous and uncontinuous mode.

Remember that artyom provides you the possibility to process the commands with a server language instead of javascript, you can enable the remote mode of artyom and use the artyom.remoteProcessorService method.

Note You'll need an SSL certificate in your website (https connection) in order to use the continuous mode, otherwise you'll be prompted for the permission to access the microphone everytime the recognition ends.

Pro tip Set always the debug property to true if you're working with artyom locally , you'll find convenient, valuable messages and information in the browser console.

Speech text

Use artyom.say to speak text. The language is retrieven at the initialization from the lang property.

Note Artyom removes the limitation of the traditional API ( about 150 characters max. Read more about this issue here ). With artyom you can read very extense text chunks without being blocked and the onEnd and onStart callbacks will be respected.

Pro tip Split the text by yourself in the way you want and execute and use artyom.say many times to decrease the probability of limitation of characters in the spoken text.

Test it by yourself paste all the text you want in the following textarea and click on speak to hear it !

Speech to text

Convert what you say into text easily with the dictation object.

Note You'll need to stop artyom before start a new dictation using artyom.fatality as 2 instances of webkitSpeechRecognition cannot run at time.

Simulate instructions without say a word

You can simulate a command without use the microphone using artyom.simulateInstruction("command identifier") for test purposes (or you don't have any microphone for test).

Try simulating any of the commands of this document like "hello","go to github" etc.

Get spoken text while artyom is active

If you want to show the user the recognized text while artyom is active, you can redirect the output of the speech recognition of artyom using artyom.redirectRecognizedTextOutput .

All that you say on this website will be shown in the following box:

Pause and resume commands recognition

You can pause the commands recognition, not the original speechRecognition. The text recognition will continue but the commands execution will be paused using the artyom.dontObey method.

To resume the command recognition use the artyom.obey . Alternatively, use the obeyKeyword property to enable with the voice at the initialization.

Useful keywords

Use the executionKeyword at the initialization to execute immediately a command though you are still talking. Use the obeyKeyword to resume the commands recognition if you use the pause method ( artyom.dontObey ). If you say this keyword while artyom is paused, artyom will be resumed and it will continue processing commands automatically.

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• Chrome for Developers

Voice driven web apps - Introduction to the Web Speech API

The new JavaScript Web Speech API makes it easy to add speech recognition to your web pages. This API allows fine control and flexibility over the speech recognition capabilities in Chrome version 25 and later. Here's an example with the recognized text appearing almost immediately while speaking.

DEMO / SOURCE

Let’s take a look under the hood. First, we check to see if the browser supports the Web Speech API by checking if the webkitSpeechRecognition object exists. If not, we suggest the user upgrades their browser. (Since the API is still experimental, it's currently vendor prefixed.) Lastly, we create the webkitSpeechRecognition object which provides the speech interface, and set some of its attributes and event handlers.

The default value for continuous is false, meaning that when the user stops talking, speech recognition will end. This mode is great for simple text like short input fields. In this demo , we set it to true, so that recognition will continue even if the user pauses while speaking.

The default value for interimResults is false, meaning that the only results returned by the recognizer are final and will not change. The demo sets it to true so we get early, interim results that may change. Watch the demo carefully, the grey text is the text that is interim and does sometimes change, whereas the black text are responses from the recognizer that are marked final and will not change.

To get started, the user clicks on the microphone button, which triggers this code:

We set the spoken language for the speech recognizer "lang" to the BCP-47 value that the user has selected via the selection drop-down list, for example “en-US” for English-United States. If this is not set, it defaults to the lang of the HTML document root element and hierarchy. Chrome speech recognition supports numerous languages (see the “ langs ” table in the demo source), as well as some right-to-left languages that are not included in this demo, such as he-IL and ar-EG.

After setting the language, we call recognition.start() to activate the speech recognizer. Once it begins capturing audio, it calls the onstart event handler, and then for each new set of results, it calls the onresult event handler.

This handler concatenates all the results received so far into two strings: final_transcript and interim_transcript . The resulting strings may include "\n", such as when the user speaks “new paragraph”, so we use the linebreak function to convert these to HTML tags <br> or <p> . Finally it sets these strings as the innerHTML of their corresponding <span> elements: final_span which is styled with black text, and interim_span which is styled with gray text.

interim_transcript is a local variable, and is completely rebuilt each time this event is called because it’s possible that all interim results have changed since the last onresult event. We could do the same for final_transcript simply by starting the for loop at 0. However, because final text never changes, we’ve made the code here a bit more efficient by making final_transcript a global, so that this event can start the for loop at event.resultIndex and only append any new final text.

That’s it! The rest of the code is there just to make everything look pretty. It maintains state, shows the user some informative messages, and swaps the GIF image on the microphone button between the static microphone, the mic-slash image, and mic-animate with the pulsating red dot.

The mic-slash image is shown when recognition.start() is called, and then replaced with mic-animate when onstart fires. Typically this happens so quickly that the slash is not noticeable, but the first time speech recognition is used, Chrome needs to ask the user for permission to use the microphone, in which case onstart only fires when and if the user allows permission. Pages hosted on HTTPS do not need to ask repeatedly for permission, whereas HTTP hosted pages do.

So make your web pages come alive by enabling them to listen to your users!

We’d love to hear your feedback...

• For comments on the W3C Web Speech API specification: email , mailing archive , community group
• For comments on Chrome’s implementation of this spec: email , mailing archive

Refer to the Chrome Privacy Whitepaper to learn how Google is handling voice data from this API.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2013-01-13 UTC.

react-speech-recognition

• 0 Dependencies
• 65 Dependents
• 41 Versions

A React hook that converts speech from the microphone to text and makes it available to your React components.

How it works

useSpeechRecognition is a React hook that gives a component access to a transcript of speech picked up from the user's microphone.

SpeechRecognition manages the global state of the Web Speech API, exposing functions to turn the microphone on and off.

Under the hood, it uses Web Speech API . Note that browser support for this API is currently limited, with Chrome having the best experience - see supported browsers for more information.

This version requires React 16.8 so that React hooks can be used. If you're used to version 2.x of react-speech-recognition or want to use an older version of React, you can see the old README here . If you want to migrate to version 3.x, see the migration guide here .

Useful links

Basic example, why you should use a polyfill with this library, cross-browser example, supported browsers, troubleshooting.

• Version 3 migration guide
• TypeScript declaration file in DefinitelyTyped

Installation

To install:

npm install --save react-speech-recognition

To import in your React code:

import SpeechRecognition, { useSpeechRecognition } from 'react-speech-recognition'

The most basic example of a component using this hook would be:

You can see more examples in the example React app attached to this repo. See Developing .

By default, speech recognition is not supported in all browsers, with the best native experience being available on desktop Chrome. To avoid the limitations of native browser speech recognition, it's recommended that you combine react-speech-recognition with a speech recognition polyfill . Why? Here's a comparison with and without polyfills:

• ✅ With a polyfill, your web app will be voice-enabled on all modern browsers (except Internet Explorer)
• ❌ Without a polyfill, your web app will only be voice-enabled on the browsers listed here
• ✅ With a polyfill, your web app will have a consistent voice experience across browsers
• ❌ Without a polyfill, different native implementations will produce different transcriptions, have different levels of accuracy, and have different formatting styles
• ✅ With a polyfill, you control who is processing your users' voice data
• ❌ Without a polyfill, your users' voice data will be sent to big tech companies like Google or Apple to be transcribed
• ✅ With a polyfill, react-speech-recognition will be suitable for use in commercial applications
• ❌ Without a polyfill, react-speech-recognition will still be fine for personal projects or use cases where cross-browser support is not needed

react-speech-recognition currently supports polyfills for the following cloud providers:

You can find the full guide for setting up a polyfill here . Alternatively, here is a quick (and free) example using Speechly:

• Install @speechly/speech-recognition-polyfill in your web app
• You will need a Speechly app ID. To get one of these, sign up for free with Speechly and follow the guide here
• Here's a component for a push-to-talk button. The basic example above would also work fine.

Detecting browser support for Web Speech API

If you choose not to use a polyfill, this library still fails gracefully on browsers that don't support speech recognition. It is recommended that you render some fallback content if it is not supported by the user's browser:

Without a polyfill, the Web Speech API is largely only supported by Google browsers. As of May 2021, the following browsers support the Web Speech API:

• Chrome (desktop): this is by far the smoothest experience
• Safari 14.1
• Microsoft Edge
• Chrome (Android): a word of warning about this platform, which is that there can be an annoying beeping sound when turning the microphone on. This is part of the Android OS and cannot be controlled from the browser
• Android webview
• Samsung Internet

For all other browsers, you can render fallback content using the SpeechRecognition.browserSupportsSpeechRecognition function described above. Alternatively, as mentioned before, you can integrate a polyfill .

Detecting when the user denies access to the microphone

Even if the browser supports the Web Speech API, the user still has to give permission for their microphone to be used before transcription can begin. They are asked for permission when react-speech-recognition first tries to start listening. At this point, you can detect when the user denies access via the isMicrophoneAvailable state. When this becomes false , it's advised that you disable voice-driven features and indicate that microphone access is needed for them to work.

Controlling the microphone

Before consuming the transcript, you should be familiar with SpeechRecognition , which gives you control over the microphone. The state of the microphone is global, so any functions you call on this object will affect all components using useSpeechRecognition .

Turning the microphone on

To start listening to speech, call the startListening function.

This is an asynchronous function, so it will need to be awaited if you want to do something after the microphone has been turned on.

Turning the microphone off

To turn the microphone off, but still finish processing any speech in progress, call stopListening .

To turn the microphone off, and cancel the processing of any speech in progress, call abortListening .

Consuming the microphone transcript

To make the microphone transcript available in your component, simply add:

Resetting the microphone transcript

To set the transcript to an empty string, you can call the resetTranscript function provided by useSpeechRecognition . Note that this is local to your component and does not affect any other components using Speech Recognition.

To respond when the user says a particular phrase, you can pass in a list of commands to the useSpeechRecognition hook. Each command is an object with the following properties:

• command : This is a string or RegExp representing the phrase you want to listen for. If you want to use the same callback for multiple commands, you can also pass in an array here, with each value being a string or RegExp
• command : The command phrase that was matched. This can be useful when you provide an array of command phrases for the same callback and need to know which one triggered it
• resetTranscript : A function that sets the transcript to an empty string
• matchInterim : Boolean that determines whether "interim" results should be matched against the command. This will make your component respond faster to commands, but also makes false positives more likely - i.e. the command may be detected when it is not spoken. This is false by default and should only be set for simple commands.
• The value of command (with any special characters removed)
• The speech that matched command
• The similarity between command and the speech
• The object mentioned in the callback description above
• fuzzyMatchingThreshold : If the similarity of speech to command is higher than this value when isFuzzyMatch is turned on, the callback will be invoked. You should set this only if isFuzzyMatch is true . It takes values between 0 (will match anything) and 1 (needs an exact match). The default value is 0.8 .
• bestMatchOnly : Boolean that, when isFuzzyMatch is true , determines whether the callback should only be triggered by the command phrase that best matches the speech, rather than being triggered by all matching fuzzy command phrases. This is useful for fuzzy commands with multiple command phrases assigned to the same callback function - you may only want the callback to be triggered once for each spoken command. You should set this only if isFuzzyMatch is true . The default value is false .

Command symbols

To make commands easier to write, the following symbols are supported:

• Example: 'I would like to order *'
• The words that match the splat will be passed into the callback, one argument per splat
• Example: 'I am :height metres tall'
• The one word that matches the named variable will be passed into the callback
• Example: 'Pass the salt (please)'
• The above example would match both 'Pass the salt' and 'Pass the salt please'

Example with commands

Continuous listening.

By default, the microphone will stop listening when the user stops speaking. This reflects the approach taken by "press to talk" buttons on modern devices.

If you want to listen continuously, set the continuous property to true when calling startListening . The microphone will continue to listen, even after the user has stopped speaking.

Be warned that not all browsers have good support for continuous listening. Chrome on Android in particular constantly restarts the microphone, leading to a frustrating and noisy (from the beeping) experience. To avoid enabling continuous listening on these browsers, you can make use of the browserSupportsContinuousListening state from useSpeechRecognition to detect support for this feature.

Alternatively, you can try one of the polyfills to enable continuous listening on these browsers.

Changing language

To listen for a specific language, you can pass a language tag (e.g. 'zh-CN' for Chinese) when calling startListening . See here for a list of supported languages.

regeneratorRuntime is not defined

If you see the error regeneratorRuntime is not defined when using this library, you will need to ensure your web app installs regenerator-runtime :

• npm i --save regenerator-runtime
• If you are using NextJS, put this at the top of your _app.js file: import 'regenerator-runtime/runtime' . For any other framework, put it at the top of your index.js file

How to use react-speech-recognition offline?

Unfortunately, speech recognition will not function in Chrome when offline. According to the Web Speech API docs : On Chrome, using Speech Recognition on a web page involves a server-based recognition engine. Your audio is sent to a web service for recognition processing, so it won't work offline.

If you are building an offline web app, you can detect when the browser is offline by inspecting the value of navigator.onLine . If it is true , you can render the transcript generated by React Speech Recognition. If it is false , it's advisable to render offline fallback content that signifies that speech recognition is disabled. The online/offline API is simple to use - you can read how to use it here .

You can run an example React app that uses react-speech-recognition with:

On http://localhost:3000 , you'll be able to speak into the microphone and see your speech as text on the web page. There are also controls for turning speech recognition on and off. You can make changes to the web app itself in the example directory. Any changes you make to the web app or react-speech-recognition itself will be live reloaded in the browser.

View the API docs here or follow the guide above to learn how to use react-speech-recognition .

• recognition

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Recognizing Speech with Vanilla JavaScript

Christopher Okoro

StackAnatomy

by Obinna Okoro

Before we start our project, I’d like to discuss the concept of speech recognition. What is speech recognition? Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that enables a program to process human speech into a written format . In today’s world, big companies, especially big tech companies, use AI’s such as Alexa, Cortana, Google Assistant, and Siri, which all have the Speech recognition feature, a key component of their performance.

In this tutorial, we will learn how to use JavaScript to add a speech recognition feature to any web app. We will be using the speech recognition Webkit API to achieve this; the chat app should look and function like this:

The chat app will be able to access your microphone when the start listening button is clicked and will have a response to specific questions asked. The chat app is only available on a few browsers on Desktop and Android.

Web Speech API is used to incorporate voice data into web apps. It provides two distinct areas of functionality — speech recognition and speech synthesis (also known as text to speech, or TTS) — which open up interesting new possibilities for accessibility and control mechanisms. It receives speech through a device’s microphone, which is then checked by a speech recognition service against a list of grammar. When a word or phrase is successfully recognized, it returns a result or results as a text string, and other actions can be launched as a result or results.

So to get started, we need to create a chat section structure with HTML and style it with CSS. Our primary focus is on the functionality of the chat section so that you can get the HTML structure and CSS styling in my GitHub repository or for practice purposes, you can create and style a chat section of your choice and follow along for the functionalities in this article.

Setting Up our JavaScript file

Head straight into the JS section, the first thing to do is grab a text container where all messages and replies will be in and the buttons that start and stop the speech recognition process, and then we set up the window speech recognition WebKit API . After setting that up, we will create a variable that will store the speech recognition constructor and set the interim results to true.

The interim results seen on line 10 allow us to get the results when we speak, so it is something like real-time. If we set it to false, it will simply wait till we are done speaking and then return the result, but for this tutorial, we want to have our results while we speak.

After setting up the window WebKit above, we can create a new element. We will create a p tag, then create an event listener below for our recognition and pass in (e) as a parameter and log (e), so we can test what we have done so far.

We added recognition.start on line 9 to allow the web browser to start listening. When you head to the web browser and hit the refresh button, you should get a pop-up request to allow your microphone access. Click on the allow button and open your browser’s terminal while you speak. You will observe that while you speak, you’ll get some events in your terminal, and if you open any of them, you’ll see some options, including results, which we need. If you also look closely, you’d observe that most events have a length of 1 while some have a length of 2. If you open the results property with a length of 2, you’d see it contains two separate words like in the picture below.

Looking at the image above, it has a length of 2 because it contains two words that I highlighted. The words are meant to be in a single sentence, and to correct that we will need to map through each of our results and put them together in one sentence. For that to happen, we will make a variable; let’s call it texts. Then we need to make the results property an array. We’ll use Array. from and then insert (e.results), and that will give us an array.

Now we need to map through the results array and target the first speech recognition result, which has an index of zero. Then we target the transcript property that holds the words, map them through, and then join both transcripts to put both words together in a sentence. If you log text and head to the terminal in your browser and start speaking, you will see our words are forming sentences, although it is not 100% accurate yet.

Open Source Session Replay

OpenReplay is an open-source, session replay suite that lets you see what users do on your web app, helping you troubleshoot issues faster. OpenReplay is self-hosted for full control over your data.

Start enjoying your debugging experience — start using OpenReplay for free .

Adding our speech to our chat section

Now that we have successfully shown the sentences in our terminal, we need to add them to our chat section. To show them in the chat section, we need to add the text variable from above to the p tag we created earlier. Then we append it to a container div element that holds the p tag in our HTML. If you check your web browser, you’d see our results are now showing in the chat section, but there is a problem. If you start speaking again, it will keep adding the sentences to just one paragraph. This is because we need to start over a new session in a new paragraph when the first session ends.

To resolve this, we will need to create an event listener with an “end” event to stop the last session and a function containing a recognition start, to begin a new session. If you speak in your browser, you will still notice that new sentences or words are overriding the old sentences or words contained in the paragraph tag, and we don’t want that too. To handle this, we also need to create a new paragraph for a new session, but before we do that, we will need to change the isFinal value, as seen below.

The isFinal property is located in the speech recognition results as seen above. It is set to false by default, meaning we are in our current session, and whenever it is true, we have ended that session. So going back to our code, we will need to check the isFinal results with a conditional statement, as seen below. When we set the isFinal property to true, a new paragraph tag will be added below with the content of the new session, and that is all.

Adding some Custom replies to our Chat-app

We have successfully set up our chat app to listen with our browser’s microphone and display what was heard in written format. I will also show you how to set the buttons to start and stop the listening process below. We can also do something exciting and create custom replies based on the texts displayed. To do this, we will have to go into our last conditional statement before the p tag and add another conditional statement. This will check if the text variable we created earlier contains a particular word like “hello”. If true, we can create a p tag, give it a class name for styling and then add a custom reply to the p tag.

We can also perform specific tasks like opening another page and a lot more. I have added a couple of replies to my code below.

The window method , as seen above, is a JS method that tells the browser to open a certain path or link. Ensure you maintain the letter casing while setting your task if needed. Once all is done, if you head to your browser and speak, for instance, say “open a YouTube page”, you should be redirected to a random page on YouTube in your browser. If this doesn’t work, check your browser settings and allow page pop-ups, which should then work. So when the start button is clicked, the chat app starts the listening process, and when the stop button is clicked, it aborts the current session.

In this tutorial, we have successfully created a chat app that listens and translates what’s heard into text format. This can be used to perform different tasks like responding with custom replies and assisting in page redirections by implementing speech recognition using JavaScript. To improve on this, feel free to challenge yourself using the speech recognition API to perform complex tasks and projects like creating a translator or a mini Ai with custom replies.

GitHub repo: https://github.com/christofa/Speech-recognition-chat-app.git

A TIP FROM THE EDITOR: For solutions specific to React, don’t miss our Make your app speak with React-Speech-kit and Voice enabled forms in React with Speechly articles.

Originally published at blog.openreplay.com on August 13, 2022.

Written by Christopher Okoro

Text to speech

Member-only story

Perform Speech Recognition in Your Javascript Applications

An introduction to web speech recognition apis.

Jennifer Fu

Better Programming

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics. It recognizes and translates spoken language into text. It is also known as automatic speech recognition (ASR), computer speech recognition, or speech to text (STT).

Machine learning (ML) is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning has provided the majority of speech recognition breakthroughs in this century. Today, speech recognition technology is everywhere, such as Apple Siri , Amazon Echo , and Google Nest .

Speech recognition, along with voice response — also known as speech synthesis, or text to speech (TTS) — is powered by Web speech APIs.

In this article, we focus on speech recognition in JavaScript applications. Speech synthesis is described in another article .

SpeechRecognition Interface

SpeechRecognition is the controller interface for the recognition service. It is called webkitSpeechRecognition in Chrome. SpeechRecognition handles the SpeechRecognitionEvent sent from…

Written by Jennifer Fu

UI tech lead who enjoys cutting-edge technologies https://www.linkedin.com/in/jennifer-fu-53357b/

Text to speech

DEV Community

Posted on Aug 22, 2022 • Updated on Aug 25, 2022

Speech Recognition with JavaScript

Cover image credits: dribbble

Some time ago, speech recognition API was added to the specs and we got partial support on Chrome, Safari, Baidu, android webview, iOS safari, samsung internet and Kaios browsers ( see browser support in detail ).

Disclaimer: This implementation won't work in Opera (as it doesn't support the constructor) and also won't work in FireFox (because it doesn't support a single thing of it) so if you're using one of those, I suggest you to use Chrome -or any other compatible browser- if you want to take a try.

Speech recognition code and PoC

Edit: I realised that for any reason it won't work when embedded so here's the link to open it directly .

The implementation I made currently supports English and Spanish just to showcase.

Quick instructions and feature overview:

• Choose one of the languages from the drop down.
• Hit the mic icon and it will start recording (you'll notice a weird animation).
• Once you finish a sentence it will write it down in the box.
• When you want it to stop recording, simply press the mic again (animation stops).
• You can also hit the box to copy the text in your clipboard.

Speech Recognition in the Browser with JavaScript - key code blocks:

This implementation currently supports the following languages for speech recognition:

If you want me to add support for more languages tell me in the comment sections and I'm updating it in a blink so you can test it on your own language 😁

That's all for today, hope you enjoyed I sure did doing that

Top comments (21)

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• Location İstanbul, Turkey
• Joined Apr 28, 2022

This is really awesome. Could you please add the Turkish language? I would definitely like to try this in my native language and use it in my projects.

• Location Spain
• Education Higher Level Education Certificate on Web Application Development
• Work Tech Lead/Lead Dev
• Joined Apr 19, 2019

• Location 3000
• Work Mr at StartUp
• Joined Aug 17, 2019

It's cool mate. Very good

Thank you! 🤖

Can u add Telugu a Indian language:)

I can try, do you know the IETF/ISO language code for it? 😁

• Location Cyprus
• Work CEO at AINIRO AS
• Joined Mar 13, 2022

Cool. I once created a speech based speech recognition thing based upon MySQL and SoundEx allowing me to create code by speaking through my headphones. It was based upon creating a hierarchical “menu” where I could say “Create button”. Then the machine would respond with “what button”, etc. The thing of course produced Hyperlambda though. I doubt it can be done without meta programming.

One thing that bothers me is that this was 5 years ago, and speech support has basically stood 100% perfectly still in all browsers since then … 😕

Not in all of them, (e.g. Opera mini, FireFox mobile), it's a nice to have in browsers, specially targeting accessibility, but screen readers for blind people do the job and, on the other hand, most implementations for any other purpose send data to a backend using streams so they can process the incoming speech plus use the user feedback to train an IA among others and without hurting the performance.

...allowing me to create code by speaking through my headphones... ... I doubt it can be done without meta programming.

I agree on this. The concept "metaprogramming" is extense and covers different ways in which it can work (or be implemented) and from its own definition it is a building block for this kind of applications.

• Location Rio de Janeiro, RJ
• Education Master Degree
• Work FullStack and Mobile Developer
• Joined May 18, 2021

Thank you 🙏. I'd like that you put in Brazilian Portuguse too.

Added both Portugal and Brazilian portuguese 😁

• Work Student
• Joined Jul 21, 2022

Thanks you 🙏. I'd like that you put in french too.

Thank you! 😁

I added support for some extra languages in the mean time 😁

• Work Technical Manager @ Gabrieli Media Group
• Joined Aug 29, 2022

Thank you very much for your useful article and implementation. Does it support Greek? Have a nice (programming) day

Hi Symeon, added support for Greek el-GR , try it out! 😃

• Education Cameroon
• Joined Aug 26, 2022

• Joined Jan 15, 2023

Can you please add urdu language

Hi @aheedkhan I'm not maintaining this anymore but feel free to fork the pen! 😄

• Joined Jul 7, 2024

Help me??? stackoverflow.com/questions/755279...

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Speech Recognition with TensorFlow.js – Voice Commands

May 11, 2022 | AI , JavaScript , Machine Learning | 0 comments

The code that accompanies this article can be received after subscription

When I was a kid every almost every superhero had a voice-controlled computer. So you can imagine how my first encounter with Alexa was a profound experience for me. The kid in me was so happy and excited. Of course, then my engineering instincts kicked in and I analyzed how these devices work.

Turned out they have neural networks that handle this complicated problem. In fact, neural networks simplified the problem so much that today it is quite easy to make one of these applications on your computer using Python . But it wasn’t always like that. The first attempts were made back in 1952. by three Bell Labs researchers.

This bundle of e-books is specially crafted for  beginners . Everything from Python basics to the deployment of Machine Learning algorithms to production in one place. Become a Machine Learning Superhero  TODAY !

They have built a system for single-speaker digit recognition with the vocabularies of 10 words . However, by the 1980s this number has grown dramatically. Vocabulary grew up to 20,000 words and first commercial products started appearing. Dragon Dictate was one of the first such products and it was originally priced at $9,000. Alexa is more affordable today, right?

However today we can perform Speech Recognition in browser with Tensorflo.js. In this article, we cover:

• Transfer Learning
• How does Speech Recognition work?
• Implementation with Tensorflow.js

1. Transfer Learning 

Historically, image classification is a problem that popularized deep neural networks especially visual types of neural networks – Convolutional neural networks (CNN) . Today, transfer learning is used for other type of machine learning tasks, like NLP and Speech Recognition. We will not go into details about what are CNNs and how they work. However, we can say that CNNs were popularized after they broke a record in  The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) back in 2012.

This competition evaluates algorithms for object detection and image classification at a large scale. The dataset that they provide contains 1000 image categories and over 1.2 million images. The goal of the image classification algorithm is to correctly predict to which class the object belongs to. Since 2012. every winner of this competition used CNNs.

Training deep neural networks can be computational and time-consuming. To get really good results, you need a lot of computing power, which means a lot of GPUs and this means…well, a lot of money. You could of course train these big architectures and get SOTA results on cloud environments, but this is also quite expensive. 

For a while, these architectures were not available for regular developers. However, the concept of transfer learning changed that. Especially, for the problem, we are solving today – image classification. Today we can use state-of-the-art architectures that won at ImageNet competition   thanks to the transfer learning and pre-trained models.

1.1 Pre-Trained Models

At this moment one might wonder “What are pre-trained models?”. Essentially, a  pre-trained model  is a saved network that was previously trained on a large dataset, for example on the  ImageNet  dataset. There are two ways in which you can use those. You can use it as the out-of-the-box solution and or you can use it with  transfer learning .   Since large datasets are usually used for some global solution you can customize a pre-trained model and  specialize   it for certain problems.

This way you can utilize some of the most famous neural networks without losing too much time and resources on  training . Additionally, you can  fine-tune  these models, by modifying the behavior of the chosen layers. The whole idea revolves around using lower layers of pre-trained CNN model and adding additional layers that will customize the architecture for the specific problems.

Essentially, serious transfer learning models are usually composed of   two parts . We call them backbone and head.   Backbone   is usually deep architecture that was pre-trained on the   ImageNet   dataset without top layers. Head is a part of the image classification model that is used for the prediction of custom classes.

These layers are added on top of the pre-trained model. With these systems, we have two phases: bottleneck and training phase. During the bottleneck phase, images of the specific dataset are run through the backbone architecture, and results are stored. During the training phase stored output from the backbone is used to train custom layers.

There are several areas where using pre-trained models is suitable and speech recognition is one of them. This model is called Speech Command Recognizer . Essentially, it is a JavaScript module that enables recognition of spoken commands comprised of simple English words.

The default vocabulary ’18w’ includes the following words: digits from “zero” to “nine”, “up”, “down”, “left”, “right”, “go”, “stop”, “yes”, “no”. Additional categories of “unknown word” and “background noise” are also available. Apart from already mentioned ’18w’ dictionary even smaller dictionary ‘ directional4w’ is available. It contains only four directional words (‘up’, ‘down’, ‘left’, ‘right’).

2. How does Speech Recognition work?

There are a lot of approaches when it comes to a combination of neural networks and audio . Speech is often handled using some sort of Recurrent Neural Networks or LSTMs . However, Speech Command Recognizer uses simple architecture that is called Convolutional Neural Networks for Small-footprint Keyword Spotting .

This approach is based on image recognition and Convolutional Neural Networks we examined in the previous article . At the first glance, that might be confusing, since audio is one a one-dimensional continuous signal across time, not a 2D spatial problem.

2.1 Spectogram

This architecture is utilizing a spectrogram . That is a visual representation of the spectrum of frequencies of a signal as it varies with time. Essentially, the window of time in which word should fit into is defined.

This is done by grouping audio signal samples into segments. When that is done, analysis of the strengths of the frequencies is done, and segments with possible words are defined. These segments are then converted into spectrograms, e.g. one-channel images that are used for word recognition:

The image that’s made using this pre-processing is then fed into a multi-layer convolutional neural network.

You have probably noticed that this page asked you for permission of using microphone . That is because we embedded implementation demo in this page. In order for this demo to work, you have to allow it to use microphone.

Now, you can use commands ‘up’, ‘down’, ‘left’ and ‘right’ to draw on the canvas below. Go ahead try it out:

4. Implementation with TensorFlow.js

4.1 html file.

First, let’s take a look into index.html file of our implementation. In one of the previous article , we presented several ways of installing  TensorFlow.js . One of them was integrating it within the script  tag of the  HTML file . That is how we will do it here as well. Apart from that, we need to add an additional script tag for the pre-trained model . Here is how index.html looks like:

JavaScript code that contains this implementation is located within script.js. This file should be located in the same folder as the index.html file. In order to run this whole process, all you have to do is open index.html inside of your browser and allow it to use your microphone. 

4.2 Script File

Now, let’s examine the scri pt.js file, where the whole implementaiton is located. Here is how the main run  function looks:

Here we can see the workflow of the application. First, we create an instance of the model and assign it to the global variable recognizer . We use ‘directional4w’ dictionary because we need only ‘up’ , ‘down’, ‘left’ and ‘right’ commands.

Then we wait for the model to be loaded. This might take some time if your internet connection is slow. Once that is done, we initialize the canvas on which drawing is performed. Finally, the predict method is called. Here is what is happening inside that function:

This method is doing the heavy lifting . In essence, it runs an endless loop in which recognizer is listening to the words you are saying. Notice that we are using parameter the probabilityThreshold .

This parameter defines should the callback function be called at all. Essentially, the callback function is invoked only if the maximum probability score is greater than this threshold. When we get the word, we get the direction in which we should draw.

Then we calculate the coordinates for the end of the line using the function calculateNewPosition . The step is 10 pixels, meaning the length of the line will be 10 pixels. You can play with both the probabilityThreshold and this length value. Once we get the new coordinates we use canvas to draw the line. That is it. Pretty straight-forward, right?

In this article, we saw how we can easily use pre-trained models of TensorFlow.js . They are a good starting point for some easy applications. We even built one example of such applications using which you can draw using voice commands. That is pretty cool and possibilities are endless. Of course, you can further train these models, get better results and use them for more complicated solutions. Meaning, you can really utilize transfer learning. However, that is a story for another time.

Thank you for reading!

Nikola M. Zivkovic

CAIO at Rubik's Code

Nikola M. Zivkovic is  the author of books:  Ultimate Guide to Machine Learning  and  Deep Learning for Programmers . He loves knowledge sharing, and he is an experienced speaker. You can find him speaking at  meetups, conferences, and as a guest lecturer at the University of Novi Sad.

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How to Add Speech Recognition to Your React and Node.js Project

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SpeechRecognition: SpeechRecognition() constructor

The SpeechRecognition() constructor creates a new SpeechRecognition object instance.

This code is excerpted from our Speech color changer example.

Specifications

Browser compatibility

BCD tables only load in the browser with JavaScript enabled. Enable JavaScript to view data.

• Web Speech API

This browser is no longer supported.

Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.

Cognitive Services Speech SDK for JavaScript

• 1 contributor

To simplify the development of speech-enabled applications, Microsoft provides the Speech SDK for use with the Speech service . The Speech SDK provides consistent native Speech-to-Text and Speech Translation APIs.

Install the npm module

Install the Cognitive Services Speech SDK npm module

The following code snippets illustrates how to do simple speech recognition from a file:

The previous example uses single-shot recognition, which recognizes a single utterance. You can also use continuous recognition to control when to stop recognizing. Check out our step-by-step quickstart for many more options.

• Step-by-step quickstart for JavaScript .
• Step-by-step quickstart for the browser .
• More samples can be found in our Speech SDK sample repository .

Azure SDK for JavaScript

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Additional resources

• Español – América Latina
• Português – Brasil
• Tiếng Việt
• TensorFlow Core

Simple audio recognition: Recognizing keywords

This tutorial demonstrates how to preprocess audio files in the WAV format and build and train a basic automatic speech recognition (ASR) model for recognizing ten different words. You will use a portion of the Speech Commands dataset ( Warden, 2018 ), which contains short (one-second or less) audio clips of commands, such as "down", "go", "left", "no", "right", "stop", "up" and "yes".

Real-world speech and audio recognition systems are complex. But, like image classification with the MNIST dataset , this tutorial should give you a basic understanding of the techniques involved.

Import necessary modules and dependencies. You'll be using tf.keras.utils.audio_dataset_from_directory (introduced in TensorFlow 2.10), which helps generate audio classification datasets from directories of .wav files. You'll also need seaborn for visualization in this tutorial.

Import the mini Speech Commands dataset

To save time with data loading, you will be working with a smaller version of the Speech Commands dataset. The original dataset consists of over 105,000 audio files in the WAV (Waveform) audio file format of people saying 35 different words. This data was collected by Google and released under a CC BY license.

Download and extract the mini_speech_commands.zip file containing the smaller Speech Commands datasets with tf.keras.utils.get_file :

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

Divided into directories this way, you can easily load the data using keras.utils.audio_dataset_from_directory .

The audio clips are 1 second or less at 16kHz. The output_sequence_length=16000 pads the short ones to exactly 1 second (and would trim longer ones) so that they can be easily batched.

The dataset now contains batches of audio clips and integer labels. The audio clips have a shape of (batch, samples, channels) .

This dataset only contains single channel audio, so use the tf.squeeze function to drop the extra axis:

The utils.audio_dataset_from_directory function only returns up to two splits. It's a good idea to keep a test set separate from your validation set. Ideally you'd keep it in a separate directory, but in this case you can use Dataset.shard to split the validation set into two halves. Note that iterating over any shard will load all the data, and only keep its fraction.

Let's plot a few audio waveforms:

Convert waveforms to spectrograms

The waveforms in the dataset are represented in the time domain. Next, you'll transform the waveforms from the time-domain signals into the time-frequency-domain signals by computing the short-time Fourier transform (STFT) to convert the waveforms to as spectrograms , which show frequency changes over time and can be represented as 2D images. You will feed the spectrogram images into your neural network to train the model.

A Fourier transform ( tf.signal.fft ) converts a signal to its component frequencies, but loses all time information. In comparison, STFT ( tf.signal.stft ) splits the signal into windows of time and runs a Fourier transform on each window, preserving some time information, and returning a 2D tensor that you can run standard convolutions on.

Create a utility function for converting waveforms to spectrograms:

• The waveforms need to be of the same length, so that when you convert them to spectrograms, the results have similar dimensions. This can be done by simply zero-padding the audio clips that are shorter than one second (using tf.zeros ).
• When calling tf.signal.stft , choose the frame_length and frame_step parameters such that the generated spectrogram "image" is almost square. For more information on the STFT parameters choice, refer to this Coursera video on audio signal processing and STFT.
• The STFT produces an array of complex numbers representing magnitude and phase. However, in this tutorial you'll only use the magnitude, which you can derive by applying tf.abs on the output of tf.signal.stft .

Next, start exploring the data. Print the shapes of one example's tensorized waveform and the corresponding spectrogram, and play the original audio:

Your browser does not support the audio element.

Now, define a function for displaying a spectrogram:

Plot the example's waveform over time and the corresponding spectrogram (frequencies over time):

Now, create spectrogram datasets from the audio datasets:

Examine the spectrograms for different examples of the dataset:

Build and train the model

Add Dataset.cache and Dataset.prefetch operations to reduce read latency while training the model:

For the model, you'll use a simple convolutional neural network (CNN), since you have transformed the audio files into spectrogram images.

Your tf.keras.Sequential model will use the following Keras preprocessing layers:

• tf.keras.layers.Resizing : to downsample the input to enable the model to train faster.
• tf.keras.layers.Normalization : to normalize each pixel in the image based on its mean and standard deviation.

For the Normalization layer, its adapt method would first need to be called on the training data in order to compute aggregate statistics (that is, the mean and the standard deviation).

Configure the Keras model with the Adam optimizer and the cross-entropy loss:

Train the model over 10 epochs for demonstration purposes:

Let's plot the training and validation loss curves to check how your model has improved during training:

Evaluate the model performance

Run the model on the test set and check the model's performance:

Display a confusion matrix

Use a confusion matrix to check how well the model did classifying each of the commands in the test set:

Run inference on an audio file

Finally, verify the model's prediction output using an input audio file of someone saying "no". How well does your model perform?

As the output suggests, your model should have recognized the audio command as "no".

Export the model with preprocessing

The model's not very easy to use if you have to apply those preprocessing steps before passing data to the model for inference. So build an end-to-end version:

Test run the "export" model:

Save and reload the model, the reloaded model gives identical output:

This tutorial demonstrated how to carry out simple audio classification/automatic speech recognition using a convolutional neural network with TensorFlow and Python. To learn more, consider the following resources:

• The Sound classification with YAMNet tutorial shows how to use transfer learning for audio classification.
• The notebooks from Kaggle's TensorFlow speech recognition challenge .
• The TensorFlow.js - Audio recognition using transfer learning codelab teaches how to build your own interactive web app for audio classification.
• A tutorial on deep learning for music information retrieval (Choi et al., 2017) on arXiv.
• TensorFlow also has additional support for audio data preparation and augmentation to help with your own audio-based projects.
• Consider using the librosa library for music and audio analysis.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-07-19 UTC.

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Speech Recognition - Run continuously

I'm trying to create an HTML5-powered voice-controlled editor using the Speech Recognition API. Currently, the problem is when you start recording, it only lasts for a certain amount of time (basically until the user stops talking).

I can set continuous and interimResults to true , but that doesn't keep it recording forever. It still ends.

I can also tell it to start again during the end event, but then it asks for permission every time, which is highly disruptive.

Is there a way to allow it to go continuously while only having to ask a user once?

• speech-recognition
• html5-audio

4 Answers 4

No matter the settings you'll choose, Google Chrome stops the speech recognition engine after a while... there's no way around it.

The only reliable solution I've found for continuous speech recognition, is to start it again by binding to the onend() event, as you've suggested.

If you try a similar technique, be aware of the following:

If you are not on HTTPS, the user will be prompted to give permission over and over again on each restart. For this, and many other reasons, don't compromise on HTTP when using Speech Recognition.

Make sure you are not restarting the speech recognition immediately onend() without some safeguards to make sure you aren't putting the browser into an endless loop (e.g. two open tabs with onend(function() {restart()}) can crash the browser, as I've detailed in this bug report: https://code.google.com/p/chromium/issues/detail?id=296690 ) See https://github.com/TalAter/annyang/blob/1ee294e2b6cb9953adb9dcccf4d3fcc7eca24c2c/src/annyang.js#L214 for how I handle this.

Don't autorestart if the reason for it ending is something like service-not-allowed or not-allowed See https://github.com/TalAter/annyang/blob/1ee294e2b6cb9953adb9dcccf4d3fcc7eca24c2c/src/annyang.js#L196

You can see how I handled this in my code - https://github.com/TalAter/annyang/blob/master/src/annyang.js

• @samanime If you believe this is the most accurate answer, please mark the answer as the correct one. –  Tal Ater Commented Jun 2, 2015 at 21:14
• 1 Just an update. The github links seem to be broken. Here is the current link I found. github.com/TalAter/annyang –  jkw4703 Commented Aug 9, 2018 at 22:31
• All links have been fixed to ones that shouldn't break with future versions of annyang –  Tal Ater Commented Aug 15, 2018 at 13:29
• 1 This answer is great! the "be aware of the following" section is very useful and goes above-and-beyond what the OP was asking –  Brian Risk Commented May 30, 2020 at 13:43

Kindly try this code, I think it does what you need:

<!DOCTYPE html> <html> <head> <title>Speech recognition</title> <style> #result{ border: 2px solid black; height: 200px; border-radius: 3px; font-size: 14px; } button{ position: absolute; top: 240px; left: 50%; } </style> <script type="application/javascript"> function start(){ var r = document.getElementById("result"); if("webkitSpeechRecognition" in window){ var speechRecognizer = new webkitSpeechRecognition(); speechRecognizer.continuous = true; speechRecognizer.interimResults = true; speechRecognizer.lang = "en-US"; speechRecognizer.start(); var finalTranscripts = ""; speechRecognizer.onresult = function(event){ var interimTranscripts = ""; for(var i=event.resultIndex; i<event.results.length; i++){ var transcript = event.results[i][0].transcript; transcript.replace("\n", "<br>"); if(event.results[i].isFinal){ finalTranscripts += transcript; } else{ interimTranscripts += transcript; } r.innerHTML = finalTranscripts + '<span style="color: #999;">' + interimTranscripts + '</span>'; } }; speechRecognizer.onerror = function(event){ }; } else{ r.innerHTML = "Your browser does not support that."; } } </script> </head> <body> <div id="result"></div> <button onclick="start()">Listen</button> </body> </html>

• I was looking for this "speechRecognizer.continuous = true;" thanks a lot! –  SoEzPz Commented Jan 6, 2019 at 3:44

HTML 5 Speech Continuously requires this...

window.SpeechRecognition = window.webkitSpeechRecognition || window.SpeechRecognition; if ('SpeechRecognition' in window) { console.log('supported speech') } else { console.error('speech not supported') } const recognition = new window.SpeechRecognition(); recognition.continuous = true; recognition.onresult = (event) => { console.log('transscript: ', event.results[event.results.length -1][0].transcript); } recognition.start();

You will have to to be rstarting the engine every few seconds.see at my code, https://github.com/servo-ai/servo-platform/blob/master/editor/src/assets/js/voice/asr.js

Note: after v 70 of chrome, a click UI is needed at least once

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Demo: JavaScript Speech Recognition

Allow access to your microphone and then say something -- the Speech Recognition API may echo back what you said! Also: check out the Dev Tools console to follow events:

You said: nothing yet .

Back to: JavaScript Speech Recognition