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Java Speech API Frequently Asked Questions

This collection of frequently asked questions (FAQ) provides brief answers to many common questions about the Java Speech API (JSAPI).

Question Index

Download questions, where can i get the java speech api (jsapi).

The Java Speech API (JSAPI) is not part of the JDK and Sun does not ship an implementation of JSAPI. Instead, we work with third party speech companies to encourage the availability of multiple implementations .

API Questions

What is the java speech api (jsapi).

The Java Speech API allows Java applications to incorporate speech technology into their user interfaces. It defines a cross-platform API to support command and control recognizers, dictation systems and speech synthesizers.

What does the Java Speech API specification include?

The Java Speech API specification includes the Javadoc-style API documentation for the approximately 70 classes and interfaces in the API. The specification also includes a detailed Programmer's Guide which explains both introductory and advanced speech application programming with JSAPI. Two companion specifications are available: JSML and JSGF.

The specification is not yet provided with the .class files needed to compile applications with JSAPI.

What are JSML and JSGF?

The Java Speech API Markup Language (JSML) and the Java Speech API Grammar Format (JSGF) are companion specifications to the Java Speech API. JSML (currently in beta) defines a standard text format for marking up text for input to a speech synthesizer. JSGF version 1.0 defines a standard text format for providing a grammar to a speech recognizer.

How was the JSAPI specification developed?

Sun Microsystems, Inc. worked in partnership with leading speech technology companies to define the initial specification of the Java Speech API, JSML and JSGF. Sun is grateful for the contributions of:

• Apple Computer, Inc.
• Dragon Systems, Inc.
• IBM Corporation
• Novell, Inc.
• Philips Speech Processing
• Texas Instruments Incorporated

How does JSAPI relate to other Java APIs?

The Java Speech API is part of a family of APIs that work together as a suite to provide customers with enhanced graphics and extended communications capabilities. These APIs include the

• Java 2D API
• Java 3D API
• Java Advanced Imaging API
• Java Sound API
• Java Telephony API

Implementation Questions

What jsapi implementations are now available.

The Java Speech API is a freely available specification and therefore anyone is welcome to develop an implementation. The following implementations are known to exist.

Note: Sun Microsystems, Inc. makes no representations or warranties about the suitability of the software listed here, either express or implied, including but not limited to the implied warranties of mechantability, fitness for a particular purpose, or non-infringement. The implementations listed here have not been tested with regard to compliance to the JSAPI specification, nor does their appearance on this page imply any form of endorsement of compliance on the part of Sun.

• Description: Open source speech synthesizer written entirely in the Java programming language.
• Requirements: JDK 1.4. Read about more requirements on the FreeTTS web site.
• Description: Implementation based on IBM's ViaVoice product, which supports continuous dictation, command and control and speech synthesis. It supports all the European language versions of ViaVoice -- US & UK English, French, German, Italian and Spanish -- plus Japanese.
• Requirements: JDK 1.1.7 or later or JDK 1.2 on Windows 95 with 32MB, or Windows NT with 48MB. Both platforms also require an installation ViaVoice 98.
• Description: Beta version of "Speech for Java" on Linux. Currently only supports speech recognition.
• Requirements: Red Hat Linux 6.0 with 32MB, and Blackdown JDK 1.1.7 with native thread support.
• Description: Implementation for use with any recognition/TTS speech engine compliant with Microsoft's SAPI5 (with SAPI4 support for TTS engines only). An additional package allows redirection of audio data to/from Files, Lines and remote clients (using the javax.sound.sampled package). Some examples demonstrate its use in applets in Netscape and IE browsers.
• Requirements: JDK 1.1 or better, Windows 98, Me, 2000 or NT, and any SAPI 5.1, 5.0 or 4.0 compliant speech engine (some of which can be downloaded from Microsoft's web site).

Lernout & Hauspie's TTS for Java Speech API

• Description: Implementations based upon ASR1600 and TTS3000 engines, which support command and control and speech synthesis. Supports 10 different voices and associated whispering voices for the English language. Provides control for pitch, pitch range, speaking rate, and volume.
• Requirements: Sun Solaris OS version 2.4 or later, JDK 1.1.5. Sun Swing package (free download) for graphical Type-n-Talk demo.
• More information: Contact Edmund Kwan , Director of Sales, Western Region Speech and Language Technologies and Solutions ([email protected])

Conversa Web 3.0

• Description: Conversa Web is a voice-enabled Web browser that provides a range of facilities for voice-navigation of the web by speech recognition and text-to-speech. The developers of Conversa Web chose to write a JSAPI implementation for the speech support.
• Requirements: Windows 95/98 or NT 4.0 running on Intel Pentium 166 MHz processor or faster (or equivalent). Minimum of 32 MB RAM (64 MB recommended). Multimedia system: sound card and speakers. Microsoft Internet Explorer 4.0 or higher.
• Description: Festival is a general multi-lingual speech synthesis system developed by the Centre for Speech Technology Research at the University of Edinburgh. It offers a full text to speech system with various APIs, as well an environment for development and research of speech synthesis techniques. It is written in C++ with a Scheme-based command interpreter for general control and provides a binding to the Java Speech API. Supports the English (British and American), Spanish and Welsh languages.
• Requirements: Festival runs on Suns (SunOS and Solaris), FreeBSD, Linux, SGIs, HPs and DEC Alphas and is portable to other Unix machines. Preliminary support is available for Windows 95 and NT. For details and requirements see the Festival download page .

Elan Speech Cube

• Description: Elan Speech Cube is a Multilingual, multichannel, cross-operating system text-to-speech software component for client-server architecture. Speech Cube is available with 2 TTS technologies (Elan Tempo : diphone concatenation and Elan Sayso : unit selection), covering 11 languages. Speech Cube native Java client supports JSAPI/JSML.
• Requirements: JDK 1.3 or later on Windows NT/2000/XP, Linux or Solaris 2.7/2.8, Speech Cube V4.2 and higher.
• About Elan Speech: Elan Speech is an established worldwide provider of text-to-speech technology (TTS). Elan TTS transforms any IT generated text into speech and reads it out loud.

How do I use JSAPI in an applet?

It is possible to use JSAPI in an applet. In order to do this, users will need the Java Plug-in (see here ). The reason for this is that JSAPI implementations require access to the AWT EventQueue, and the built-in JDK support in the browsers we've worked with denies any applet access to the AWT EventQueue. The Java Plug-in doesn't have this restriction, and users can configure the Java Plug-in to grant or deny applet access to the AWT Queue.

If you are using JRE 1.1:

Have your users follow these steps if your applet is based upon JDK 1.1:

• Obtain a JDK 1.1.7 or better Java Runtime Environment (JRE). The reason for this is we have had problems with applet security being denied with JDK 1.1.6. Please note that the user needs the JRE and not the JDK. The JRE is freely available for download from the following URL:
• Before running the browser, have the user modify their CLASSPATH environment variable to include the supporting classes for JSAPI. For example, if the user has IBM's Speech for Java, have the user include the ibmjs.jar file in CLASSPATH.
• Make sure any shared libraries for the JSAPI support are in the user's PATH. For example, if the user has IBM's Speech For Java, have the user include the ibmjs lib directory in their PATH (e.g., c:\ibmjs\lib).
• Have the user copy the speech.properties to their home directory. A user can determine their home directory by enabling the console for the Java Plug-in. When the user accesses a page that uses the Java Plug-in, the Java Plug-in console will tell the user what it thinks the user's home directory is.
• Use javakey to add your identity to their signature database (i.e., identitydb.obj). This will tell the Java Plug-in to trust applets signed by you.
• Copy the identitydb.obj that was created or updated in previous step to the user's home directory (the same place where the user copied speech.properties).

Then perform these steps on your applet:

• Use javakey to both create a signature database for your system and to sign your applet's jar file. This will allow the applet to participate in the security model.
• Create an HTML page that uses your applet in the Plug-in.
• If the user experiences a "checkread" exception while attempting to run your applet, it's most likely due to a mismatch between the user's identitydb.obj file and the signature on your applet's jar file. A way to remedy this is to recreate your identitydb.obj and re-sign your jar file.

If you are using JRE 1.2:

The Java 2 platform's security model allows signing as done with JDK 1.1, but it also permits finer grained access control. The following are just some examples, and we recommend you read the Java Security Architecture Specification at the following URL before deciding what to do:

For a quick start, have your users do the following if your applet uses the Java 2 (i.e., JDK 1.2) platform:

• Obtain the JDK 1.2 Plug-in.
• Grant all applets the AllPermission property. This is extremely dangerous and is only provided as an example. To do this, have the user modify their java.policy file to contain only the following lines: grant { permission java.security.AllPermission; }
• Grant permissions to a particular URL (e.g., the URL containing your applet). To do this, have the user add the following lines to their java.policy file: grant codeBase "http://your.url.here" { permission java.security.AllPermission; }

The information in this FAQ is not meant to be a complete tutorial on the JDK 1.1 and JDK 1.2 architecture. Instead, it is meant to be hopefully enough to get you started with running JSAPI applets in a browser. We suggest you visit the following URLs to obtain more information on the Java Security models:

Java Security Home Page: link

Tutorial on JDK 1.1 Security: link

Tutorial on JDK 1.2 Security: link

Why does Netscape Navigator or Internet Explorer throw a security exception when I use JSAPI in an applet?

JSAPI implementations require access to the AWT EventQueue. The built-in Java platform support in the browsers we've worked with denies an applet access to the AWT EventQueue. As a result, JSAPI implementations will be denied access to the AWT EventQueue. In addition, we are not aware of a way to configure the built-in Java platform support in these environments to allow access to the AWT EventQueue.

The Java Plug-in (see link ), however, can be configured to allow an applet the necessary permissions it needs to use an implementation of JSAPI. As a result, we currently recommend using the Java Plug-in for applets that use JSAPI.

I'm concerned about JSAPI applets "bugging" my office. What are the plans for JSAPI and security on JDK 1.2?

The JSAPI 1.0 specification includes the SpeechPermission class that currently only supports one SpeechPermission: javax.speech. When that permission is granted, an application or applet has access to all the capabilities provided by installed speech recognizers and synthesizers. Without that permission, an application or applet has no access to speech capabilities.

As speech technology matures it is anticipated that a finer-grained permission model will be introduced to provide access by applications and applets to some, but not all, speech capabilities.

Before granting speech permission, developers and users should consider the potential impact of the grant.

Does JSAPI allow me to control the audio input source of a recognizer or redirect the audio output of a speech synthesizer?

This support is currently not in JSAPI. We plan to use the Java Sound API to help provide this support in the future. We purposely left room for expansion in the javax.speech.AudioManager interface and will further investigate this support after the Java Sound API is finalized.

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Converting Text to Speech in Java

Java Speech API: The Java Speech API allows Java applications to incorporate speech technology into their user interfaces. It defines a cross-platform API to support command and control recognizers, dictation systems and speech synthesizers.

Java Speech supports speech synthesis which means the process of generating spoken the language by machine on the basis of written input.

It is important to keep in mind that Java Speech is only a specification i.e. no implementation is included. Thus third-parties provide the implementations. The javax.speech package defines the common functionality of recognizers, synthesizers, and other speech engines. The package javax.speech.synthesis extends this basic functionality for synthesizers.

We will understand that what is required for java API to convert text to speech

• Engine: The Engine interface is available inside the speech package.”Speech engine” is the generic term for a system designed to deal with either speech input or speech output. import javax.speech.Engine;
• Central: Central provides the ability to locate, select and create speech recognizers and speech synthesizers. import javax.speech.Central;
• SynthesizerModeDesc: SynthesizerModeDesc extends the EngineModeDesc with the properties that are specific to speech synthesizers. import javax.speech.synthesis.SynthesizerModeDesc;
• Synthesizer: The Synthesizer interface provides primary access to speech synthesis capabilities.SynthesizerModeDesc adds two properties: List of voices provided by the synthesizer Voice to be loaded when the synthesizer is started. import javax.speech.synthesis.Synthesizer;

Below is an open-source implementation of Java Speech Synthesis called FreeTTS in the form of steps:

• Download the FreeTTS in the form of zip folder from here
• Extract the zip file and go to freetts-1.2.2-bin/freetts-1.2/lib/jsapi.exe
• Open the jsapi.exe file and install it.
• This will create a jar file by the name jsapi.jar . This is the JAR library that contains the FreeTTS library to be included in the project.
• Create a new Java project in your IDE.
• Include this jsapi.jar file into your project.
• Now copy the below code into your project
• Execute the project to get the below expected output.

Below is the code for the above project:

References:

• https://docs.oracle.com/cd/E17802_01/products/products/java-media/speech/forDevelopers/jsapi-doc/javax/speech/package-summary.html
• https://www.javatpoint.com/q/5931/java-code-for-converting-audio-to-text-and-video-to-audio
• http://www.oracle.com/technetwork/java/jsapifaq-135248.html

Related article: Convert Text to Speech in Python

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Introduction to the Java Speech API

By Nathan Tippy, OCI Senior Software Engineer

Speech Synthesis

Speech synthesis, also known as text-to-speech (TTS) conversion, is the process of converting text into human recognizable speech based on language and other vocal requirements. Speech synthesis can be used to enhance the user experience in many situations but care must be taken to ensure the user is comfortable with its use.

Speech synthesis has proven to be a great benefit in many ways. It is often used to assist the visually impaired as well as provide safety and efficiency in situations where the user needs to keep his eyes focused elsewhere. In the most successful applications of speech synthesis it is often central to the product requirements. If it is added on as an afterthought or a novelty it is rarely appreciated; people have high expectations when it comes to speech.

Natural sounding speech synthesis has been the goal of many development teams for a long time, yet it remains a significant challenge. People learn to speak at a very young age and continue to use their speaking and listening skills over the course of their lives, so it is very easy for people to recognize even the most minor flaws in speech synthesis.

As humans it is easy to take for granted our ability to speak but it is really a very complex process. There are a few different ways to implement a speech synthesis engine but in general they all complete the following steps:

There are many voices available to developers today. Most of them are very good and a few are quite exceptional in how natural they sound. I put together a collection of both  commercial and non-commercial voices  so you can listen to them without having to setup or install anything.

Unfortunately the best voices (as of the time of this writing) are commercial so works produced using them can not be re-distributed without fees. Depending on how many voices you use and what you are using them for the annual costs for distribution rights can run from hundreds to thousands each year. Many vendors also provide different fee schedules for distributing applications that use a voice verses audio files and/or streams produced from the voices.

Java Speech API (JSAPI)

The goal of JSAPI is to enable cross-platform development of voice applications. The JSAPI enables developers to write applications that do not depend on the proprietary features of one platform or one speech engine.

Decoupling the engine from the application is important. As you can hear from the  voice demo page ; there is a wide variety of voices with different characteristics. Some users will be comfortable with a deep male voice while others may be more comfortable with a British female voice. The choice of speech engine and voice is subjective and may be expensive. In most cases, end users will use a single speech engine for multiple applications so they will expect any new speech enabled applications to integrate easily.

The Java Speech API 1.0 was first released by Sun in 1998 and defines packages for both speech recognition and speech synthesis. In order to remain brief the remainder of the article will focus on the speech synthesis package but if you would like to know more about speech recognition visit the  CMU Sphinx sourceforge.net project .

All the JSAPI implementations available today are compliant with 1.0 or a subset of 1.0 but work is progressing on  version 2.0 (JSR113) of the API . We will be using the open source implementation from  FreeTTS  for our demo app but there are other implementations such as the one from  Cloudscape  which provides support for the SAPI5 voices that Microsoft Windows uses.

Important Classes and Interfaces

Class: javax.speech.Central

This singleton class is the main interface for access to the speech engine facilities. It has a bad name (much too generic) but as part of the upgrade to version 2.0 they will be renaming it to  EngineManager  which is a much better name based on what it does.

For our example, we will only use the availableSynthesizers and createSynthesizer methods. Both of these methods need a mode description which is the next class we will use.

Class: javax.speech.synthesis.SynthesiserModeDesc

This simple bean holds all the required properties of the Synthesizer. When requesting a specific Synthesizer or a list of available Synthesizers this object can be passed in with specific properties to restrict the results to Synthesizers matching the defined properties only. The list of properties include the engine name, mode name, locale and running synthesizer.

The mode name property is not implemented with a type safe enumeration and it should only be set to the string value 'general' or 'time' when using the FreeTTS implementation. The mode name is specific to the engine, and in this case restricts the synthesizer to those that can speak any text or those that can only speak the time. If a time-only synthesizer is used for reading general text it will attempt to read it and print error messages when those phonemes it can't pronounce are encountered.

The locale property can be used to restrict international synthesizers which have support for many languages. See the MBROLA project for some international examples.

The running synthesizer property is used to limit the synthesizers returned to only those that are already loaded into memory. Because some synthesizers can take a long time to load into memory this feature may be helpful in limiting runtime delays.

Class: javax.speech.synthesis.Synthesiser

This class is used for converting text into speech using the selected voice. Synthesizers must be allocated before they can be used and this may take some time if high quality voices are supported which make use of large data files. It is recommended that the  allocate  method is called upon startup from a background thread. Call  deallocate  only when the application is about to exit. Once you have an allocated synthesizer it can be kept for the life of the application. Please note, in the chart below, the allocating and deallocating states that the synthesizer will be in while completing the allocate and deallocate operations, respectively.

Class: javax.speech.synthesis.Voice

This simple bean holds the properties of the voice. The name, age and gender can be set along with a Boolean to indicate that only voices already loaded into memory should be used. The  setVoice  method uses these properties to select a voice matching the required properties. After a voice is selected the  getVoice  method can be called to get the properties of the voice currently being used.

Note that the age and gender parameters are integers and do not use a typesafe enumeration. If an invalid value is used a  PropertyVetoException  will be thrown. The valid constants for these fields are found on the Voice class and they are.

Interface: javax.speech.synthesis.Speakable

This interface should be implemented by any object that will produce marked up text that is to be spoken. The  specification for JSML  can be found on line and is very similar to  W3Cs Speech Synthesis Markup Language Specification (SSML)  which will be used instead of JSML for the 2.0 release.

Interface: javax.speech.synthesis.SpeakableListener

This interface should be implemented by any object wishing to listen to speech events. Notifications for events such as starting, stopping, pausing, resuming and others can be used to keep the application in sync with what the speech engine is doing.

Hello World

To try the demo you will need to set up the following:

Download  freetts-1.2.1-bin.zip  from  http://sourceforge.net/projects/freetts/   FreeTTS only supports a subset of 1.0 but it works well and has an easy-to-understand voice. Our JSML inflections will be ignored but the markup will be parsed correctly.

Unzip the freetts-1.2.1-bin.zip file to a local folder. The D:\apps\ folder will be used for this example

Go to  D:\apps\freetts-1.2.1\lib  and run jsapi.exe This will create the jsapi.jar from Sun Microsystems. This is done because it uses a different license than FreeTTS's BSD license.

Add this new jar and all the other jars found in the  D:\apps\freetts-1.2.1\lib  folder to your path. This will give us the engine, the JSAPI interfaces and three voices to use in our demo.

Copy the  D:\apps\freetts-1.2.1\speech.properties  file to your  %user.home%  or  %java.home%/lib  folders. This file is used by JSAPI to determine which speech engine will be used.

Compile the three demo files below and run BriefVoiceDemo from the command line.

BriefVoiceDemo.java

• package com.ociweb.jsapi ;
• import java.beans.PropertyVetoException ;
• import java.io.File ;
• import java.text.DateFormat ;
• import java.text.SimpleDateFormat ;
• import java.util.Date ;
• import java.util.Locale ;
• import javax.speech.AudioException ;
• import javax.speech.Central ;
• import javax.speech.EngineException ;
• import javax.speech.EngineList ;
• import javax.speech.EngineModeDesc ;
• import javax.speech.EngineStateError ;
• import javax.speech.synthesis.JSMLException ;
• import javax.speech.synthesis.Speakable ;
• import javax.speech.synthesis.SpeakableListener ;
• import javax.speech.synthesis.Synthesizer ;
• import javax.speech.synthesis.SynthesizerModeDesc ;
• import javax.speech.synthesis.Voice ;
• public class BriefVoiceDemo {
• Synthesizer synthesizer ;
• public static void main ( String [ ] args ) {
• //default synthesizer values
• SynthesizerModeDesc modeDesc = new SynthesizerModeDesc (
• null , // engine name
• "general" , // mode name use 'general' or 'time'
• Locale . US , // locale, see MBROLA Project for i18n examples
• null , // prefer a running synthesizer (Boolean)
• null ) ; // preload these voices (Voice[])
• //default voice values
• Voice voice = new Voice (
• "kevin16" , //name for this voice
• Voice. AGE_DONT_CARE , //age for this voice
• Voice. GENDER_DONT_CARE , //gender for this voice
• null ) ; //prefer a running voice (Boolean)
• boolean error = false ;
• for ( int r = 0 ; r < args. length ; r ++ ) {
• String token = args [ r ] ;
• String value = token. substring ( 2 ) ;
• //overide some of the default synthesizer values
• if ( token. startsWith ( "-E" ) ) {
• modeDesc. setEngineName ( value ) ;
• } else if ( token. startsWith ( "-M" ) ) {
• modeDesc. setModeName ( value ) ;
• } else
• //overide some of the default voice values
• if ( token. startsWith ( "-V" ) ) {
• voice. setName ( value ) ;
• } else if ( token. startsWith ( "-GF" ) ) {
• voice. setGender ( Voice. GENDER_FEMALE ) ;
• } else if ( token. startsWith ( "-GM" ) ) {
• voice. setGender ( Voice. GENDER_MALE ) ;
• //dont recognize this value so flag it and break out
• System . out . println ( token +
• " was not recognized as a supported parameter" ) ;
• error = true ;
• //The example starts here
• BriefVoiceDemo briefExample = new BriefVoiceDemo ( ) ;
• if ( error ) {
• System . out . println ( "BriefVoiceDemo -E<ENGINENAME> " +
• "-M<time|general> -V<VOICENAME> -GF -GM" ) ;
• //list all the available voices for the user
• briefExample. listAllVoices ( ) ;
• System . exit ( 1 ) ;
• //select synthesizer by the required parameters
• briefExample. createSynthesizer ( modeDesc ) ;
• //print the details of the selected synthesizer
• briefExample. printSelectedSynthesizerModeDesc ( ) ;
• //allocate all the resources needed by the synthesizer
• briefExample. allocateSynthesizer ( ) ;
• //change the synthesisers state from PAUSED to RESUME
• briefExample. resumeSynthesizer ( ) ;
• //set the voice
• briefExample. selectVoice ( voice ) ;
• //print the details of the selected voice
• briefExample. printSelectedVoice ( ) ;
• //create a listener to be notified of speech events.
• SpeakableListener optionalListener = new BriefListener ( ) ;
• //The Date and Time can be spoken by any of the selected voices
• SimpleDateFormat formatter = new SimpleDateFormat ( "h mm" ) ;
• String dateText = "The time is now " + formatter. format ( new Date ( ) ) ;
• briefExample. speakTextSynchronously ( dateText, optionalListener ) ;
• //General text like this can only be spoken by general voices
• if ( briefExample. isModeGeneral ( ) ) {
• //speak plain text
• String plainText =
• "Hello World, This is an example of plain text," +
• " any markup like <jsml></jsml> will be spoken as is" ;
• briefExample. speakTextSynchronously ( plainText, optionalListener ) ;
• //speak marked-up text from Speakable object
• Speakable speakableExample = new BriefSpeakable ( ) ;
• briefExample. speakSpeakableSynchronously ( speakableExample,
• optionalListener ) ;
• //must deallocate the synthesizer before leaving
• briefExample. deallocateSynthesizer ( ) ;
•   * Select voice supported by this synthesizer that matches the required
•   * properties found in the voice object. If no matching voice can be
•   * found the call is ignored and the previous or default voice will be used.
•   * @param voice required voice properties.
• private void selectVoice ( Voice voice ) {
• synthesizer. getSynthesizerProperties ( ) . setVoice ( voice ) ;
• } catch ( PropertyVetoException e ) {
• System . out . println ( "unsupported voice" ) ;
• exit ( e ) ;
•   * This method prepares the synthesizer for speech by moving it from the
•   * PAUSED state to the RESUMED state. This is needed because all newly
•   * created synthesizers start in the PAUSED state.
•   * See Pause/Resume state diagram.
•   * The pauseSynthesizer method is not shown but looks like you would expect
•   * and can be used to pause any speech in process.
• private void resumeSynthesizer ( ) {
• //leave the PAUSED state, see state diagram
• synthesizer. resume ( ) ;
• } catch ( AudioException e ) {
•   * The allocate method may take significant time to return depending on the
•   * size and capabilities of the selected synthesizer. In a production
•   * application this would probably be done on startup with a background thread.
•   * This method moves the synthesizer from the DEALLOCATED state to the
•   * ALLOCATING RESOURCES state and returns only after entering the ALLOCATED
•   * state. See Allocate/Deallocate state diagram.
• private void allocateSynthesizer ( ) {
• //ensure that we only do this when in the DEALLOCATED state
• if ( ( synthesizer. getEngineState ( ) & Synthesizer . DEALLOCATED ) != 0 )
• //this call may take significant time
• synthesizer. getEngineState ( ) ;
• synthesizer. allocate ( ) ;
• } catch ( EngineException e ) {
• e. printStackTrace ( ) ;
• } catch ( EngineStateError e ) {
•   * deallocate the synthesizer. This must be done before exiting or
•   * you will run the risk of having a resource leak.
•   * This method moves the synthesizer from the ALLOCATED state to the
•   * DEALLOCATING RESOURCES state and returns only after entering the
•   * DEALLOCATED state. See Allocate/Deallocate state diagram.
• private void deallocateSynthesizer ( ) {
• //ensure that we only do this when in the ALLOCATED state
• if ( ( synthesizer. getEngineState ( ) & Synthesizer . ALLOCATED ) != 0 )
• //free all the resources used by the synthesizer
• synthesizer. deallocate ( ) ;
•   * Helper method to ensure the synthesizer is always deallocated before
•   * existing the VM. The synthesiser may be holding substantial native
•   * resources that must be explicitly released.
•   * @param e exception to print before exiting.
• private void exit ( Exception e ) {
• deallocateSynthesizer ( ) ;
•   * create a synthesiser with the required properties. The Central class
•   * requires the speech.properties file to be in the user.home or the
•   * java.home/lib folders before it can create a synthesizer.
•   * @param modeDesc required properties for the created synthesizer
• private void createSynthesizer ( SynthesizerModeDesc modeDesc ) {
• //Create a Synthesizer with specified required properties.
• //if none can be found null is returned.
• synthesizer = Central. createSynthesizer ( modeDesc ) ;
• catch ( IllegalArgumentException e1 ) {
• e1. printStackTrace ( ) ;
• } catch ( EngineException e1 ) {
• if ( synthesizer == null ) {
• System . out . println ( "Unable to create synthesizer with " +
• "the required properties" ) ;
• System . out . println ( ) ;
• System . out . println ( "Be sure to check that the \" speech.properties \" " +
• " file is in one of these locations:" ) ;
• System . out . println ( " user.home : " + System . getProperty ( "user.home" ) ) ;
• System . out . println ( " java.home/lib : " + System . getProperty ( "java.home" )
• + File . separator + "lib" ) ;
•   * is the selected synthesizer capable of speaking general text
•   * @return is Mode General
• private boolean isModeGeneral ( ) {
• String mode = this . synthesizer . getEngineModeDesc ( ) . getModeName ( ) ;
• return "general" . equals ( mode ) ;
•   * Speak the marked-up text provided by the Speakable object and wait for
•   * synthesisers queue to empty. Support for specific markup tags is
•   * dependent upon the selected synthesizer. The text will be read as
•   * though the mark up was not present if unsuppored tags are encounterd by
•   * the selected synthesizer.
•   * @param speakable
•   * @param optionalListener
• private void speakSpeakableSynchronously (
• Speakable speakable,
• SpeakableListener optionalListener ) {
• this . synthesizer . speak ( speakable, optionalListener ) ;
• } catch ( JSMLException e ) {
• //wait for the queue to empty
• this . synthesizer . waitEngineState ( Synthesizer . QUEUE_EMPTY ) ;
• } catch ( IllegalArgumentException e ) {
• } catch ( InterruptedException e ) {
•   * Speak plain text 'as is' and wait until the synthesizer queue is empty
•   * @param plainText that will be spoken ignoring any markup
•   * @param optionalListener will be notified of voice events
• private void speakTextSynchronously ( String plainText,
• this . synthesizer . speakPlainText ( plainText, optionalListener ) ;
•   * Print all the properties of the selected voice
• private void printSelectedVoice ( ) {
• Voice voice = this . synthesizer . getSynthesizerProperties ( ) . getVoice ( ) ;
• System . out . println ( "Selected Voice:" + voice. getName ( ) ) ;
• System . out . println ( " Style:" + voice. getStyle ( ) ) ;
• System . out . println ( " Gender:" + genderToString ( voice. getGender ( ) ) ) ;
• System . out . println ( " Age:" + ageToString ( voice. getAge ( ) ) ) ;
•   * Helper method to convert gender constants to strings
•   * @param gender as defined by the Voice constants
•   * @return gender description
• private String genderToString ( int gender ) {
• switch ( gender ) {
• case Voice. GENDER_FEMALE :
• return "Female" ;
• case Voice. GENDER_MALE :
• return "Male" ;
• case Voice. GENDER_NEUTRAL :
• return "Neutral" ;
• case Voice. GENDER_DONT_CARE :
• return "Unknown" ;
•   * Helper method to convert age constants to strings
•   * @param age as defined by the Voice constants
•   * @return age description
• private String ageToString ( int age ) {
• switch ( age ) {
• case Voice. AGE_CHILD :
• return "Child" ;
• case Voice. AGE_MIDDLE_ADULT :
• return "Middle Adult" ;
• case Voice. AGE_NEUTRAL :
• case Voice. AGE_OLDER_ADULT :
• return "OlderAdult" ;
• case Voice. AGE_TEENAGER :
• return "Teenager" ;
• case Voice. AGE_YOUNGER_ADULT :
• return "Younger Adult" ;
• case Voice. AGE_DONT_CARE :
•   * Print all the properties of the selected synthesizer
• private void printSelectedSynthesizerModeDesc ( ) {
• EngineModeDesc description = this . synthesizer . getEngineModeDesc ( ) ;
• System . out . println ( "Selected Synthesizer:" + description. getEngineName ( ) ) ;
• System . out . println ( " Mode:" + description. getModeName ( ) ) ;
• System . out . println ( " Locale:" + description. getLocale ( ) ) ;
• System . out . println ( " IsRunning:" + description. getRunning ( ) ) ;
•   * List all the available synthesizers and voices.
• public void listAllVoices ( ) {
• System . out . println ( "All available JSAPI Synthesizers and Voices:" ) ;
• //Do not set any properties so all the synthesizers will be returned
• SynthesizerModeDesc emptyDesc = new SynthesizerModeDesc ( ) ;
• EngineList engineList = Central. availableSynthesizers ( emptyDesc ) ;
• //loop over all the synthesizers
• for ( int e = 0 ; e < engineList. size ( ) ; e ++ ) {
• SynthesizerModeDesc desc = ( SynthesizerModeDesc ) engineList. get ( e ) ;
• //loop over all the voices for this synthesizer
• Voice [ ] voices = desc. getVoices ( ) ;
• for ( int v = 0 ; v < voices. length ; v ++ ) {
• System . out . println (
• desc. getEngineName ( ) +
• " Voice:" + voices [ v ] . getName ( ) +
• " Gender:" + genderToString ( voices [ v ] . getGender ( ) ) ) ;

BriefSpeakable.java

•  * Simple Speakable
•  * Returns marked-up text to be spoken
• public class BriefSpeakable implements Speakable {
•   * Returns marked-up text. The markup is used to help the vice engine.
• public String getJSMLText ( ) {
• return "<jsml><para>This Speech <sayas class='literal'>API</sayas> " +
• "can integrate with <emp> most </emp> " +
• "of the speech engines on the market today.</para>" +
• "<break msecs='300'/><para>Keep on top of the latest developments " +
• "by reading all you can about " +
• "<sayas class='literal'>JSR113</sayas></para></jsml>" ;
•   * Implemented so the listener can print out the source
• public String toString ( ) {
• return getJSMLText ( ) ;

BriefListener.java

• import javax.speech.synthesis.SpeakableEvent ;
•  * Simple SpeakableListener
•  * Prints event type and the source object's toString()
• public class BriefListener implements SpeakableListener {
• private String formatEvent ( SpeakableEvent event ) {
• return event. paramString ( ) + ": " + event. getSource ( ) ;
• public void markerReached ( SpeakableEvent event ) {
• System . out . println ( formatEvent ( event ) ) ;
• public void speakableCancelled ( SpeakableEvent event ) {
• public void speakableEnded ( SpeakableEvent event ) {
• public void speakablePaused ( SpeakableEvent event ) {
• public void speakableResumed ( SpeakableEvent event ) {
• public void speakableStarted ( SpeakableEvent event ) {
• public void topOfQueue ( SpeakableEvent event ) {
• public void wordStarted ( SpeakableEvent event ) {

Further work on version 2.0 continues under JSR 113. The primary goal of the upcoming 2.0 spec is to bring JSAPI to J2ME but a few other overdue changes like class renaming have been done as well.

My impression after using JSAPI is that it would be much easier to use if it implemented unchecked exceptions. This would help make the code much easier to read and implement. Overall I think the API is on the right track and adds a needed abstraction layer for any project using speech synthesis.

As computer performance continues to improve and Java becomes embedded in more devices, interfaces that make computers easier for non-technical people such as voice synthesis and recognition will become ubiquitous. I recommend that anyone who might be working with embedded Java in the future keep an eye on JSR113.

• [1] JSML http://java.sun.com/products/java-media/speech/forDevelopers/JSML/
• [2] FreeTTS JSAPI setup http://freetts.sourceforge.net/
• [3] JSAPI http://java.sun.com/products/java-media/speech/news/index.html   JSAPI Guide   JSAPI JavaDoc   Overview
• [4] Diagrams http://JavaNut.com/BlogDraw

Easy Way to Learn Speech Recognition in Java With a Speech-To-Text API

Rev › Blog › Resources › Other Resources › Speech-to-Text APIs › Easy Way to Learn Speech Recognition in Java With a Speech-To-Text API

Here we explain show how to use a speech-to-text API with two Java examples.

We will be using the Rev AI API ( free for your first 5 hours ) that has two different speech-to-text API’s:

• Asynchronous API – For pre-recorded audio or video
• Streaming API – For live (streaming) audio or video

Asynchronous Rev AI API Java Code Example

We will use the Rev AI Java SDK located here .  We use this short audio , on the exciting topic of HR recruiting.

First, sign up for Rev AI for free and get an access token.

Create a Java project with whatever editor you normally use.  Then add this dependency to the Maven pom.xml manifest:

The code sample below is here . We explain it and show the output.

Submit the job from a URL:

Most of the Rev AI options are self-explanatory, for the most part.  You can use the callback to kick off downloading the transcription in another program that is on standby, listening on http, if you don’t want to use the polling method we use in this example.

Put the program in a loop and check the job status.  Download the transcription when it is done.

The SDK returns captions as well as text.

Here is the complete code:

It responds:

You can get the transcript with Java.

Or go get it later with curl, noting the job id from stdout above.

This returns the transcription in JSON format: 

Streaming Rev AI API Java Code Example

A stream is a websocket connection from your video or audio server to the Rev AI audio-to-text entire.

We can emulate this connection by streaming a .raw file from the local hard drive to Rev AI.

One Ubuntu run:

Download the audio then convert it to .raw format as shown below.  Converted it from wav to raw with the following ffmpeg command:

As you run that is gives key information about the audio file:

To explain, first we set a websocket connection and start streaming the file:

The important items to set here are the  sampling rate (not bit rate) and format.  We match this information from ffmpeg:    Audio: pcm_f32le, 48000 Hz , 

After the client connects, the onConnected event sends a message.  We can get the jobid from there.  This will let us download the transcription later if we don’t want to get it in real-time.

To get the transcription in real time, listen for the onHypothesis event:

Here is what the output looks like:

What is the Best Speech Recognition API for Java?

Accuracy is what you want in a speech-to-text API, and Rev AI is a one-of-a-kind speech-to-text API in that regard.

You might ask, “So what?  Siri and Alexa already do speech-to-text, and Google has a speech cloud API.”

That’s true.  But there’s one game-changing difference: 

The data that powers Rev AI is manually collected and carefully edited .  Rev pays 50,000 freelancers to transcribe audio & caption videos for its 99% accurate transcription & captioning services . Rev AI is trained with this human-sourced data, and this produces transcripts that are far more accurate than those compiled simply by collecting audio, as Siri and Alexa do.

Rev AI’s accuracy is also snowballing, in a sense. Rev’s speech recognition system and API is constantly improving its accuracy rates as its dataset grows and the world-class engineers constantly improve the product.

Labelled Data and Machine Learning

Why is human transcription important?

If you are familiar with machine learning then you know that converting audio to text is a classification problem.  

To train the computer to transcribe audio ML programmers feed feature-label data into their model.  This data is called a training set .

Features (sound) are input and labels (the corresponding letter) are output, calculated by the classification algorithm.

Alexa and Siri vacuum up this data all day long.  So you would think they would have the largest and therefore most accurate training data.  

But that’s only half of the equation.  It takes many hours of manual work to type in the labels that correspond to the audio.  In other words, a human must listen to the audio and type the corresponding letter and word.  

This is what Rev AI has done.

It’s a business model that has taken off, because it fills a very specific need.

For example, look at closed captioning on YouTube.  YouTube can automatically add captions to it’s audio.  But it’s not always clear.  You will notice that some of what it says is nonsense. It’s just like Google Translate: it works most of the time, but not all of the time.

The giant tech companies use statistical analysis, like the frequency distribution of words, to help their models.

But they are consistently outperformed by manually trained audio-to-voice training models.

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Using the Web Speech API

Speech recognition.

Speech recognition involves receiving speech through a device's microphone, which is then checked by a speech recognition service against a list of grammar (basically, the vocabulary you want to have recognized in a particular app.) When a word or phrase is successfully recognized, it is returned as a result (or list of results) as a text string, and further actions can be initiated as a result.

The Web Speech API has a main controller interface for this — SpeechRecognition — plus a number of closely-related interfaces for representing grammar, results, etc. Generally, the default speech recognition system available on the device will be used for the speech recognition — most modern OSes have a speech recognition system for issuing voice commands. Think about Dictation on macOS, Siri on iOS, Cortana on Windows 10, Android Speech, etc.

Note: On some browsers, such as 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.

To show simple usage of Web speech recognition, we've written a demo called Speech color changer . When the screen is tapped/clicked, you can say an HTML color keyword, and the app's background color will change to that color.

To run the demo, navigate to the live demo URL in a supporting mobile browser (such as Chrome).

HTML and CSS

The HTML and CSS for the app is really trivial. We have a title, instructions paragraph, and a div into which we output diagnostic messages.

The CSS provides a very simple responsive styling so that it looks OK across devices.

Let's look at the JavaScript in a bit more detail.

Prefixed properties

Browsers currently support speech recognition with prefixed properties. Therefore at the start of our code we include these lines to allow for both prefixed properties and unprefixed versions that may be supported in future:

The grammar

The next part of our code defines the grammar we want our app to recognize. The following variable is defined to hold our grammar:

The grammar format used is JSpeech Grammar Format ( JSGF ) — you can find a lot more about it at the previous link to its spec. However, for now let's just run through it quickly:

• The lines are separated by semicolons, just like in JavaScript.
• The first line — #JSGF V1.0; — states the format and version used. This always needs to be included first.
• The second line indicates a type of term that we want to recognize. public declares that it is a public rule, the string in angle brackets defines the recognized name for this term ( color ), and the list of items that follow the equals sign are the alternative values that will be recognized and accepted as appropriate values for the term. Note how each is separated by a pipe character.
• You can have as many terms defined as you want on separate lines following the above structure, and include fairly complex grammar definitions. For this basic demo, we are just keeping things simple.

Plugging the grammar into our speech recognition

The next thing to do is define a speech recognition instance to control the recognition for our application. This is done using the SpeechRecognition() constructor. We also create a new speech grammar list to contain our grammar, using the SpeechGrammarList() constructor.

We add our grammar to the list using the SpeechGrammarList.addFromString() method. This accepts as parameters the string we want to add, plus optionally a weight value that specifies the importance of this grammar in relation of other grammars available in the list (can be from 0 to 1 inclusive.) The added grammar is available in the list as a SpeechGrammar object instance.

We then add the SpeechGrammarList to the speech recognition instance by setting it to the value of the SpeechRecognition.grammars property. We also set a few other properties of the recognition instance before we move on:

• SpeechRecognition.continuous : Controls whether continuous results are captured ( true ), or just a single result each time recognition is started ( false ).
• SpeechRecognition.lang : Sets the language of the recognition. Setting this is good practice, and therefore recommended.
• SpeechRecognition.interimResults : Defines whether the speech recognition system should return interim results, or just final results. Final results are good enough for this simple demo.
• SpeechRecognition.maxAlternatives : Sets the number of alternative potential matches that should be returned per result. This can sometimes be useful, say if a result is not completely clear and you want to display a list if alternatives for the user to choose the correct one from. But it is not needed for this simple demo, so we are just specifying one (which is actually the default anyway.)

Starting the speech recognition

After grabbing references to the output <div> and the HTML element (so we can output diagnostic messages and update the app background color later on), we implement an onclick handler so that when the screen is tapped/clicked, the speech recognition service will start. This is achieved by calling SpeechRecognition.start() . The forEach() method is used to output colored indicators showing what colors to try saying.

Receiving and handling results

Once the speech recognition is started, there are many event handlers that can be used to retrieve results, and other pieces of surrounding information (see the SpeechRecognition events .) The most common one you'll probably use is the result event, which is fired once a successful result is received:

The second line here is a bit complex-looking, so let's explain it step by step. The SpeechRecognitionEvent.results property returns a SpeechRecognitionResultList object containing SpeechRecognitionResult objects. It has a getter so it can be accessed like an array — so the first [0] returns the SpeechRecognitionResult at position 0. Each SpeechRecognitionResult object contains SpeechRecognitionAlternative objects that contain individual recognized words. These also have getters so they can be accessed like arrays — the second [0] therefore returns the SpeechRecognitionAlternative at position 0. We then return its transcript property to get a string containing the individual recognized result as a string, set the background color to that color, and report the color recognized as a diagnostic message in the UI.

We also use the speechend event to stop the speech recognition service from running (using SpeechRecognition.stop() ) once a single word has been recognized and it has finished being spoken:

Handling errors and unrecognized speech

The last two handlers are there to handle cases where speech was recognized that wasn't in the defined grammar, or an error occurred. The nomatch event seems to be supposed to handle the first case mentioned, although note that at the moment it doesn't seem to fire correctly; it just returns whatever was recognized anyway:

The error event handles cases where there is an actual error with the recognition successfully — the SpeechRecognitionErrorEvent.error property contains the actual error returned:

Speech synthesis

Speech synthesis (aka text-to-speech, or TTS) involves receiving synthesizing text contained within an app to speech, and playing it out of a device's speaker or audio output connection.

The Web Speech API has a main controller interface for this — SpeechSynthesis — plus a number of closely-related interfaces for representing text to be synthesized (known as utterances), voices to be used for the utterance, etc. Again, most OSes have some kind of speech synthesis system, which will be used by the API for this task as available.

To show simple usage of Web speech synthesis, we've provided a demo called Speak easy synthesis . This includes a set of form controls for entering text to be synthesized, and setting the pitch, rate, and voice to use when the text is uttered. After you have entered your text, you can press Enter / Return to hear it spoken.

To run the demo, navigate to the live demo URL in a supporting mobile browser.

The HTML and CSS are again pretty trivial, containing a title, some instructions for use, and a form with some simple controls. The <select> element is initially empty, but is populated with <option> s via JavaScript (see later on.)

Let's investigate the JavaScript that powers this app.

Setting variables

First of all, we capture references to all the DOM elements involved in the UI, but more interestingly, we capture a reference to Window.speechSynthesis . This is API's entry point — it returns an instance of SpeechSynthesis , the controller interface for web speech synthesis.

Populating the select element

To populate the <select> element with the different voice options the device has available, we've written a populateVoiceList() function. We first invoke SpeechSynthesis.getVoices() , which returns a list of all the available voices, represented by SpeechSynthesisVoice objects. We then loop through this list — for each voice we create an <option> element, set its text content to display the name of the voice (grabbed from SpeechSynthesisVoice.name ), the language of the voice (grabbed from SpeechSynthesisVoice.lang ), and -- DEFAULT if the voice is the default voice for the synthesis engine (checked by seeing if SpeechSynthesisVoice.default returns true .)

We also create data- attributes for each option, containing the name and language of the associated voice, so we can grab them easily later on, and then append the options as children of the select.

Older browser don't support the voiceschanged event, and just return a list of voices when SpeechSynthesis.getVoices() is fired. While on others, such as Chrome, you have to wait for the event to fire before populating the list. To allow for both cases, we run the function as shown below:

Speaking the entered text

Next, we create an event handler to start speaking the text entered into the text field. We are using an onsubmit handler on the form so that the action happens when Enter / Return is pressed. We first create a new SpeechSynthesisUtterance() instance using its constructor — this is passed the text input's value as a parameter.

Next, we need to figure out which voice to use. We use the HTMLSelectElement selectedOptions property to return the currently selected <option> element. We then use this element's data-name attribute, finding the SpeechSynthesisVoice object whose name matches this attribute's value. We set the matching voice object to be the value of the SpeechSynthesisUtterance.voice property.

Finally, we set the SpeechSynthesisUtterance.pitch and SpeechSynthesisUtterance.rate to the values of the relevant range form elements. Then, with all necessary preparations made, we start the utterance being spoken by invoking SpeechSynthesis.speak() , passing it the SpeechSynthesisUtterance instance as a parameter.

In the final part of the handler, we include an pause event to demonstrate how SpeechSynthesisEvent can be put to good use. When SpeechSynthesis.pause() is invoked, this returns a message reporting the character number and name that the speech was paused at.

Finally, we call blur() on the text input. This is mainly to hide the keyboard on Firefox OS.

Updating the displayed pitch and rate values

The last part of the code updates the pitch / rate values displayed in the UI, each time the slider positions are moved.

|  |  |  |  |
|	|

Class javax.speech.Central

	( require)           List objects for available recognition engine modes that match the required properties.
	( require)           List objects for available synthesis engine modes that match the required properties.
	( require)           Create a with specified required properties.
	( require)           Create a with specified required properties.
	( className)           Register a speech engine with the class for use by the current application.

, , , , , , , , , ,

createRecognizer

Availablerecognizers, createsynthesizer, availablesynthesizers, registerenginecentral.

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Java Tutorial

Control statements, java object class, java inheritance, java polymorphism, java abstraction, java encapsulation, java oops misc.

(TTS) or is a type of assistive technology (it is a term for assistive, adaptive, and rehabilitative devices for people with disabilities) that reads digital text audibly. is an advanced functionality of smart devices like ATMs, online translators, text scanners, etc. Implementing text-to-speech technology in the application enhances the customer experience because of relevant accessibility. Nowadays, it is widely using in to make books audible. Even a popular platform named providing thousands of books in audio form by using the TTS technology. Most of the smart devices are coming with this feature. In this section, we will discuss and how can we Java provides the that incorporates speech technology in UI. It defines a cross-platform API to support command and control recognizers, dictation systems, and speech synthesizers. It is not a part of JDK. It is a third-party speech API to encourage the availability of multiple implementations. The architecture of the TTS system is shown in the following figure. (Java Speech API Markup Language) and (Java Speech API Grammar Format). JSML defines the standard text format for marking up text for input to a speech synthesizer. While the JSGF defines the standard text format for providing grammar to a speech recognizer. The following figure illustrates the block diagram of text-to-speech. It is a parent interface for all speech engines that is defined in the package. The speech engine includes Recognizer and a synthesizer. Therefore, it deals with both the speech input and speech output. The and methods are used to create speech engines. Both methods accept a single parameter that defines the required properties for the engine to be created. The parameter may be one of the subclasses i.e. or . A mode descriptor defines a set of required properties for an engine. For example, a can describe a Synthesizer for that has a male voice. Similarly, a can describe a Recognizer that supports dictation for . It is a class that belongs to javax.speech package. It is the initial access point for all speech input and output proficiencies. It provides the ability to locate, select, and create speech recognizers and speech synthesizers. It extends the with the properties that are specific to speech synthesizers. It is also an interface that provides primary access to speech synthesis capabilities. adds two properties: provided by the synthesizer when the synthesizer is started. Java provides the following third-party Speech API that can be used to convert text to speech. In this section, we will discuss the widely used speech synthesis API called . is an open-source speech synthesis system that is written entirely in Java programming language. It is based on also known as CMU Flite. It is a small, fast run-time open source text to speech synthesis engine. By using the FreeTTS API, we can make our computer speak. In other words, we can say that it is an artificial production of human speech that converts a normal text to speech. In order to create a Java program, first, we need to download and install API. Follow the steps given below. Download the in zip form. Extract the zip file that provides two folders, as we have shown in the following image. Access the directory Install the by double-clicking on the file. Accept the License Agreement by clicking on the button. button. The above process generates a jar file (in the same location where the jsapi.exe file resides) named It is a jar file that contains the FreeTTS library that is required to create a text-to-speech application. Now, we will create a Java project in IDE as usually we create. In our case, we have created a Java project with the name In this project, we have created a class name and write the following code. Navigate the directory and copy the file and paste the properties file into the home directory. In our case the directory is Now run the above program. The output of the program cannot be shown here because it is only audible. So, try it yourself. JSAPI also allows us to set , and of the voice by using the and methods, respectively. For example, consider the following Java program. In the following program, note that instead of using the package, we have used package.

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Text to Speech API Python: A Comprehensive Guide

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Table of contents, prerequisites, installing dependencies, google cloud text-to-speech setup, using google cloud text-to-speech, using gtts (google text-to-speech), real-time text-to-speech, language support, audio encoding, configuring voice parameters, linux and windows, source code and documentation.

Text-to-speech ( TTS ) technology has significantly advanced, allowing developers to create high-quality audio from text inputs using various programming languages, including Python. This article will guide you through the process of setting up and using a TTS API in Python, covering installation, configuration, and usage with code examples. We will explore various APIs, including Google Cloud Text-to-Speech and open-source alternatives like gTTS. Whether you need English, French, German, Chinese, or Hindi, this tutorial has got you covered.

Before we start, ensure you have Python 3 installed on your system. You can download it from the official Python website . Additionally, you'll need pip, the Python package installer, which is included with Python 3.

To begin, you'll need to install the required Python libraries. Open your command-line interface (CLI) and run the following command:

These libraries will allow you to interact with the Google Cloud Text-to-Speech API and the open-source gTTS library.

• Step 1 : Create a Google Cloud Project: First, create a project on the Google Cloud Console .
• Step 2 : Enable the Text-to-Speech API: Navigate to the API Library and enable the Google Cloud Text-to-Speech API.
• Step 3 : Create Service Account and API Key: Create a service account and download the JSON key file. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to point to this file:

Here's a "Hello World" example using the Google Cloud Text-to-Speech API:

This code synthesizes speech from text and saves it as an MP3 file.

For a simpler and open-source alternative, you can use gTTS. Here's a basic example:

To achieve real-time TTS, you can integrate the TTS API with applications that require instant feedback, such as voice assistants or chatbots.

Advanced Configuration and Parameters

Google Cloud Text-to-Speech supports various languages, including English (en-US), French (fr-FR), German (de-DE), Chinese (zh-CN), and Hindi (hi-IN). You can change the language_code parameter in the synthesize_text function to use different languages.

The audio_encoding parameter supports different formats such as MP3, WAV, and FLAC. Modify the AudioConfig accordingly.

You can customize voice parameters such as pitch, speaking rate, and volume gain. For example:

Using the TTS API with Other Platforms

You can integrate the TTS API with Android applications using HTTP requests to the Google Cloud Text-to-Speech API.

The provided Python examples work seamlessly on both Linux and Windows platforms.

Find the complete source code and detailed documentation on GitHub and Google Cloud Text-to-Speech documentation .

In this tutorial, we've covered the basics of setting up and using Text-to-Speech APIs in Python, including Google Cloud Text-to-Speech and gTTS. Whether you need high-quality speech synthesis for English, French, German, Chinese, or Hindi, these tools provide robust solutions. Explore further configurations and parameters to enhance your applications and achieve real-time TTS integration.

By following this guide, you should now be able to convert text to high-quality audio files using Python, enabling you to create engaging and accessible applications.

What is the free text to speech API for Python?

The free text-to-speech API for Python is gTTS (Google Text-to-Speech), an open-source library that allows you to convert text to speech using Google's TTS API.

Can Python do text to speech?

Yes, Python can perform text-to-speech using libraries such as gTTS and the Google Cloud Text-to-Speech API, which utilize speech recognition and artificial intelligence technologies.

How to use Google Text to Speech API in Python?

To use Google Text to Speech API in Python, install the client library, set up your API key, and use the texttospeech SDK to synthesize speech; refer to the quickstart guide for detailed steps.

Is Google Text to Speech API free?

Google Text to Speech API offers a free tier with limited usage, but for extensive use, pricing terms apply; it provides low latency and high-quality speech synthesis suitable for various machine learning and artificial intelligence applications.

Celebrity Voice Generators: A How to

Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

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Releases: lkuza2/java-speech-api

Version 2.0 release candidate 3.

• Fixed bug in Google Translate
• Restored Recognizer to working order

Version 2.0 release candidate 2

• Fixed and updated Google endpoints to the latest version. Synthesiser and Translator now works!
• Improved Google Duplex Speech recognition by utilizing continuous and interim modes.
• Added a speech recognition demo so people can try it for themselves.

Version 2.0 release candidate

This is version 2.0 supporting all the new APIs. While this has been in beta for months, I think it's finally time to add some binaries to make the project more accessible. Enjoy.

Version 1.12

A great deal of prep has been done for the V2 version. Note that many features are still experimental such as the Duplex API. Release Highlights:

• A critical bug fix regarding language auto detection has also been included in this release.
• A bug fix in the Microphone class regarding passive listening.
• The Synthesiser is now significantly faster than before.

Version 1.11

Updated release with bug fixes and Google Translate service added.

Version 1.10

This binary brings together a plethora of new improvements to virtually every feature in the project. From a completely rewritten Synthesiser class to the new Microphone Analyzer class. This release also brings forward the improvements to various improvements including the re-branding to J.A.R.V.I.S. Java Speech API. Virtually every feature has been improved or rewritten in this release. Please see the changelog for full details.

This release was completely worked on by Skylion, and he deserves all credit for the great work done creating this amazing release.

Version 1.05

Changes from pull request from @duncanj

Precompiled jar with libraries and javadoc is zipped and attached for this release

Changes include: Improved language support for recognizer (Credits to @duncanj ) Add support for multiple responses for recognizer (Credits to @duncanj ) Add profanity filter toggle support for recognizer (Credits to @duncanj )

Converting Text to Speech in JavaScript

JavaScript, as a versatile programming language primarily used for client-side web development, plays a crucial role in TTS conversion within web-based applications. With the advent of browser-based APIs such as the Web Speech API, JavaScript empowers developers to integrate TTS functionality directly into web pages without the need for external plugins or software dependencies.

JavaScript’s role in TTS conversion encompasses various aspects, including text processing, API integration, and user interaction. Developers can manipulate text elements within the document object model (DOM), extract content dynamically from web pages, and pass it to the browser’s speech synthesis engine for audio output. JavaScript facilitates the configuration of speech parameters such as voice selection, rate, pitch, and volume, allowing for customizable TTS experiences tailored to user preferences.

Table of Contents

Step 1: selecting the target text, step 2: utilizing browser speech synthesis api, step 3: configuring speech parameters, step 4: implementing error handling, accessibility enhancement, seamless integration with web applications, platform independence, real-time feedback and interaction, educational applications for children, accessibility features in websites and web apps, language learning platforms, interactive storytelling applications, personal productivity tools, assistive technology for the elderly, audio-guided tours and navigation apps, accessibility enhancements in gaming applications, steps to convert text to speech with javascript.

Text to speech functionality in JavaScript primarily relies on the Web Speech API, a standardized interface that enables web developers to integrate speech synthesis capabilities into their applications. The Web Speech API provides a set of interfaces and methods for generating natural-sounding speech directly within the browser environment.

The central component of the Web Speech API is the Speech Synthesis interface, which serves as the entry point for initiating and controlling the speech synthesis process. Through this interface, developers can create instances of the Speech Synthesis Utterance object, configure speech parameters, select voices, and trigger the synthesis of spoken output. 

To begin the text to speech conversion process, developers must identify the specific text content they wish to render audibly. This can include static text content within HTML elements or dynamically generated text retrieved from data sources or user interactions.

Once the target text is identified, developers can initiate the speech synthesis process using the Speech Synthesis interface. This involves creating an instance of the Speech SynthesisUtterance object, which encapsulates the text to be spoken and provides additional configuration options.

The SpeechSynthesisUtterance object allows developers to customize various aspects of the synthesized speech, including voice, language, rate, pitch, and volume. By invoking methods and setting properties on the SpeechSynthesisUtterance object, developers can fine-tune the characteristics of the spoken output to suit user preferences and application requirements.

Error handling is an essential aspect of robust text to speech implementation. Developers should anticipate and handle potential errors that may arise during the speech synthesis process, such as network connectivity issues, unsupported speech synthesis features, or voice selection errors.

By incorporating error-handling mechanisms, developers can gracefully handle unexpected scenarios and provide users with informative feedback when issues occur.

Benefits of Using JavaScript for TTS Conversion

JavaScript offers numerous advantages for implementing TTS conversion, making it a preferred choice for developers seeking to enhance accessibility and user experience within web applications. Let’s explore some of the key benefits of using JavaScript for TTS conversion:

One of the primary benefits of using JavaScript for TTS conversion is the significant enhancement of accessibility within web applications. By integrating TTS functionality, developers empower users with visual impairments or reading difficulties to access and interact with content more effectively.

JavaScript’s versatility and compatibility with web technologies make it well-suited for seamless integration of TTS functionality into web applications. Developers can leverage JavaScript frameworks and libraries to streamline the implementation process and enhance the overall user experience.

JavaScript-based TTS solutions offer platform independence, allowing users to access speech synthesis functionality across different devices and operating systems without the need for additional software or plugins. This ensures a consistent user experience and broadens the reach of TTS-enabled applications.

JavaScript-powered TTS functionality enables real-time feedback and interaction within web applications, enhancing user engagement and interactivity. By providing audio feedback in response to user actions or input, developers can create immersive and responsive user experiences.

Top Use Cases of JavaScript Text to Speech

TTS functionality in JavaScript opens up several possibilities for enhancing user experiences and accessibility across various applications. Here are the top eight use cases of text to speech in JavaScript:

JavaScript-based TTS proves invaluable in educational apps tailored for children, offering an interactive audio platform for learning letters, numbers, and basic vocabulary. Through engaging audio feedback, children not only absorb information but also develop language skills in a fun and immersive manner, fostering a deeper understanding of educational concepts.

The integration of TTS into websites and web applications serves as a lifeline for users with visual impairments or reading difficulties. By offering audio alternatives to on-screen text content, websites become more inclusive and accessible, ensuring that all users can effortlessly navigate and engage with digital content.

TTS functionality serves as a cornerstone in language learning platforms, aiding learners in mastering pronunciation , vocabulary, and listening comprehension. By accurately pronouncing words, phrases, and sentences in different languages, TTS technology provides invaluable support for language learners at all levels.

JavaScript-powered TTS browser supports interactive storytelling experiences, enriching narratives with vibrant characters, dialogues, and narrations. By giving voice to characters as the browser speaks, HTML elements and storytelling applications captivate users and immerse them in compelling narratives, fostering engagement and imagination.

TTS integration in personal productivity tools revolutionizes task management and note-taking, offering users a hands-free solution to manage schedules, reminders, and notes within the operating system. With TTS-enabled productivity tools, users can effortlessly stay organized and productive, enhancing efficiency and accessibility in daily tasks with browser support in the HTML file.

TTS features in assistive technology applications offer a lifeline to the elderly by reading messages, alerts, and notifications. By improving communication, speech recognition , and accessibility, TTS-enabled assistive technology enhances the quality of life for older users, empowering them to stay connected and engaged in the digital world.

JavaScript-based text input guides users through audio-guided tours and navigation apps, providing contextual information about landmarks, points of interest, and directions. With SpeechSynthesis API, TTS-enabled navigation apps enhance the user experience, making travel and tourism more accessible and enjoyable.

TTS technology enhances accessibility in gaming applications by providing audio cues, text input instructions, and narrations. By offering auditory feedback, TTS-enabled gaming applications cater to users with disabilities, ensuring an inclusive and immersive gaming experience for all players with a Javascript file and SpeechSynthesis API.

As technology continues to evolve, there is a growing need for further exploration and implementation of TTS solutions in various domains. Developers are encouraged to explore innovative ways to integrate TTS functionality into their applications, pushing the boundaries of accessibility, usability, and user experience.

The ongoing advancements in TTS technology, coupled with the versatility of JavaScript, present exciting opportunities for future development in converting text to speech. From enhancing e-learning platforms and gaming experiences to improving customer service interactions and facilitating language learning, the possibilities for TTS integration are endless in modern browsers.

How to use text to speech in JavaScript?

To convert text to speech in JavaScript, you can utilize the Web Speech API. First, you create a SpeechSynthesisUtterance object, set the text you want to speak, configure speech parameters like voice and rate, and then use the SpeechSynthesis.speak() method to trigger the speech synthesis.

How to add voice to text in JavaScript?

Adding voice to text in JavaScript involves using the Web Speech API. You create a SpeechRecognition object, configure it, and then listen for speech input using events like 'result’. Once the speech is recognized, you can extract the text and convert text to speech accordingly in your javascript code.

Is JavaScript TTS compatible with all browsers?

The Web Speech API for JavaScript TTS is supported in most modern browsers, including Chrome, Firefox, Safari, and Edge. However, it’s essential to check browser compatibility for speech recognition and consider fallback options for older browsers or non-standard environments.

How can I integrate JavaScript TTS into my website?

To integrate JavaScript TTS into your website, follow these steps: Firstly, check browser compatibility for Web Speech API support. Next, implement TTS functionality using SpeechSynthesisUtterance and SpeechSynthesis.speak() methods. Customize speech parameters like voice, rate, and pitch to enhance user experience. Trigger TTS output based on user interactions or application logic. Finally, thoroughly test TTS functionality across different browsers and devices to ensure compatibility and usability. You can thus incorporate JavaScript TTS into your website and provide users with accessible and interactive auditory content.

How to convert text into voice in JavaScript?

In JavaScript text to speech, you can use the SpeechSynthesisUtterance interface provided by the Web Speech API. First, create a SpeechSynthesisUtterance object, set the text content you want to convert into speech, configure speech parameters if needed, and then use the SpeechSynthesis.speak() method to initiate the speech synthesis process.

You should also read:

Twitch Text to Speech: Steps to Set up Twitch TTS with Ease 

How to create engaging videos using TikTok text to speech

An in-depth Guide on How to Use Text to Speech on Discord

• AI Meeting Assistant
• Communication and collaboration
• Contact center tips
• Tips and best practices
• App tutorials

Best Speech-to-Text API Solutions in 2024

4. Post-Processing and Error Correction:

Speech-to-text api frequently asked questions.

Spread the word

APIs are revolutionizing the way we interact with technology.

By converting spoken language into written text, these APIs open new possibilities for accessibility, productivity, and user interaction across numerous platforms and devices. As we delve into the intricacies of speech-to-text technology, it’s essential to understand both the foundational components and the advanced mechanisms that drive these systems.

The purpose of this article is to delve into the best speech-to-text API solutions available in 2024 , focusing on their technical aspects, industry applications, and advantages.

What is Behind Speech-to-Text API Technology?

Speech-to-text APIs have become an integral part of modern technology, enabling a wide range of applications from automated transcriptions to voice-controlled interfaces. Understanding the underlying technology helps in appreciating the complexity and the advancements that make these APIs so powerful. Here’s a deep dive into the technical aspects of speech-to-text API technology:

Core Components of Speech-to-Text Technology

1. automatic speech recognition (asr):.

• Phoneme Recognition: Identifying the smallest units of sound in speech.
• Feature Extraction: Converting raw audio signals into a format that the ASR system can process, typically involving the extraction of features like Mel-frequency cepstral coefficients (MFCCs).
• N-gram Models: Probabilistic models that predict the next word in a sequence based on the previous ‘n’ words.
• Neural Language Models: Use deep learning to predict word sequences with greater context and accuracy.

2. Deep Learning and Neural Networks:

• Recurrent Neural Networks (RNNs): Specialized for sequence data, RNNs are adept at processing sequences of audio signals. Variants like Long Short-Term Memory (LSTM) networks are particularly effective in handling long-range dependencies in speech.
• Convolutional Neural Networks (CNNs): Primarily used for image processing, CNNs have found applications in speech recognition by helping to identify features in audio spectrograms.
• Transformer Models: The latest advancement in deep learning, transformer models use attention mechanisms to focus on important parts of the input sequence, significantly improving the accuracy and efficiency of speech-to-text systems.

3. Real-Time Processing:

• Streaming APIs: Enable continuous transcription of audio in real-time, which is essential for applications like live captioning and interactive voice response systems.
• On-Device Processing: Reduces latency and dependency on cloud services by performing speech recognition directly on the user’s device. This approach is particularly beneficial for applications requiring immediate response and enhanced privacy.
• Text Normalization: Converts transcribed text into a more readable format by addressing issues like punctuation, capitalization, and spacing.
• Contextual Understanding: Advanced speech-to-text systems incorporate contextual understanding to correct errors based on the surrounding text, improving the overall accuracy of the transcription.

Speech-to-Text APIs Industry Applications

Speech-to-text technology is utilized across various industries, each benefiting from its unique capabilities. Here is a table summarizing the applications in different industries:

Industry	Speech-to-Text API Application
	Automates the transcription of patient records. Enables hands-free operation of medical devices.
	Provides real-time transcription of customer interactions. Enhances AI-powered customer service tools.
	Automates the generation of captions for video content. Assists in the transcription of interviews and podcasts.
	Provides students with accurate transcriptions of lectures. Enhances language learning apps with accurate feedback.

Advancements in Speech-to-Text Technology

Recent advancements have significantly improved the capabilities of speech-to-text APIs:

• Multilingual Support: Modern APIs support a wide range of languages and dialects, making them accessible to a global audience.
• Enhanced Accuracy: Continuous improvements in deep learning models and large-scale datasets have led to higher transcription accuracy.
• Privacy and Security: On-device processing and encrypted data transmission ensure that user data remains secure, addressing privacy concerns.

Challenges and Future Directions

While speech-to-text technology has come a long way, it still faces several challenges:

• Accurate Transcription in Noisy Environments: Background noise can significantly impact the accuracy of transcriptions. Advanced noise-cancellation algorithms and robust acoustic models are being developed to address this issue.
• Dialect and Accent Variability: Ensuring accurate transcription across different dialects and accents remains a challenge. Ongoing research focuses on creating more inclusive models that can handle diverse speech patterns.
• Real-Time Translation: Integrating speech-to-text with real-time translation presents both a challenge and an opportunity. Achieving seamless translation while maintaining accuracy is a key area of development.

Here are some of the top speech-to-text API solutions available in 2024, based on extensive research from reputable sources such as Deepgram, AssemblyAI, and others​​:

1. Assembly AI

Assembly AI is a leading provider of speech-to-text solutions, known for its high accuracy and advanced machine learning models. It supports multiple languages and dialects, making it a versatile choice for various industries.

Assembly AI

• High accuracy with advanced machine learning models.
• Support for multiple languages and dialects.
• Real-time and batch processing capabilities.
• Excellent accuracy for various accents and dialects.
• Flexible integration options with APIs and SDKs.
• Robust support and documentation.
• Requires significant computational resources for processing.
• Limited offline capabilities.

Use Cases: Suitable for transcription services, call centers, and media industries.

2. Deepgram

Deepgram offers deep learning-based ASR with customizable models, providing high accuracy and fast processing speeds. It integrates seamlessly with various platforms, making it ideal for voice assistants and call analytics.

• Deep learning-based ASR with customizable models.
• High accuracy and fast processing speeds.
• Integration with various platforms via APIs.
• Highly scalable for large-scale applications.
• Offers real-time and batch processing options.
• Supports multiple languages and dialects.
• Customization may require technical expertise.
• Premium features can be costly.

Use Cases: Ideal for voice assistants, transcription, and call analytics.

3. Speechmatics

Speechmatics is renowned for its universal speech recognition technology, offering high accuracy across diverse accents and dialects. It is particularly useful for enterprise applications, providing scalable solutions for various industries.

Speechmatics

• Universal speech recognition with high accuracy.
• Support for diverse accents and dialects.
• Scalable solutions for enterprise applications.
• Highly accurate transcription across various dialects.
• Strong enterprise support and scalability.
• Continuous improvements and updates.
• Setup can be complex for new users.
• Higher cost for extensive usage.

Use Cases: Useful for broadcast media, telecommunication, and transcription services.

Rev AI stands out with its industry-leading accuracy, offering human-reviewed options for even higher precision. It supports real-time and asynchronous transcription, making it perfect for media production and legal sectors.

• Industry-leading accuracy with human-reviewed options.
• Real-time and asynchronous transcription.
• Easy integration with SDKs and APIs.
• Highly accurate transcriptions with human review.
• Versatile integration options for various platforms.
• Strong reputation in the industry.
• Human-reviewed transcriptions can be more expensive.
• Limited free tier options.

Use Cases: Perfect for media production, legal, and education sectors.

Whisper, developed by OpenAI, is a cutting-edge speech recognition technology offering high accuracy and robust performance. It supports multiple languages and is ideal for developers seeking open-source solutions.

• OpenAI’s cutting-edge speech recognition technology.
• High accuracy and robust performance.
• Support for multiple languages.
• Open-source and customizable.
• Strong performance across various languages.
• Free to use with extensive documentation.
• May require fine-tuning for specific applications.
• Limited support compared to commercial solutions.

Use Cases: Suitable for developers seeking open-source solutions for diverse applications.

Symbl offers advanced conversational intelligence with contextual understanding, providing real-time transcription and analysis. It integrates well with communication platforms, making it ideal for customer service and team collaboration.

• Conversational intelligence with contextual understanding.
• Real-time transcription and analysis.
• Integration with communication platforms.
• Advanced contextual understanding enhances transcription accuracy.
• Seamless integration with various communication tools.
• Offers real-time insights and analytics.
• Can be complex to integrate without technical expertise.
• Some features are available only in premium plans.

Use Cases: Ideal for customer service, sales, and team collaboration tools.

Krisp: The Ultimate Transcription Solution for Call Centers

Krisp is a versatile and reliable transcription software designed to enhance call center operations and improve customer service.

Technical Advantages of Krisp for Enterprise Call Centers

Superior transcription accuracy.

• 96% Accuracy:  Leveraging cutting-edge AI, Krisp ensures high-quality transcriptions even in noisy environments, boasting a Word Error Rate (WER) of only 4%.

On-Device Processing

• Enhanced Security:  Krisp’s desktop app processes transcriptions and noise cancellation directly on your device, ensuring sensitive information remains secure and compliant with stringent security standards.

Unmatched Privacy

• Real-Time Redaction:  Ensures the utmost privacy by redacting Personally Identifiable Information (PII) and Payment Card Information (PCI) in real-time.
• Private Cloud Storage:  Stores transcripts in a private cloud owned by customers, with write-only access, ensuring complete control over data.

Centralized Solution Across All Platforms

• Cost Optimization:  By centralizing call transcriptions across all platforms, Krisp CCT optimizes costs and simplifies data management.
• Streamlined Operations:  Eliminates the need for multiple transcription services, making data handling more efficient.

No Additional Integrations Required

• Effortless Integration:  Krisp’s plug-and-play setup integrates seamlessly with major Contact Center as a Service (CCaaS) and Unified Communications as a Service (UCaaS) platforms.
• Operational Efficiency:  Requires no additional configurations, ensuring smooth and secure operations from the start.

Use Cases Enabled by Krisp Call Center Transcription

Use Case	Description
	Boost your BPO’s efficiency by ensuring quality control of customer interactions, enabling targeted training and coaching sessions, refining sales strategies, and improving call center metrics for an enhanced operation.
	Maintain regulatory compliance and adhere to industry standards with Krisp CCT, which provides a searchable record of all customer interactions. This can support your compliance efforts and offer valuable information for dispute resolution.
	Streamline customer research and analysis, identify actionable customer insights, and collect feature requests to better understand and serve your customers.
	Identify fraudulent patterns in customer interactions, mitigate data breaches, and enhance fraud prevention strategies to protect your business and customers with Krisp CCT.

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Java API for speech to text conversion

For developing an desktop based app, I am looking for speech to text conversion third party lib in Java. (open source will be preferred)

Anybody aware of such API which will be flexible and extendable?

• speech-recognition

• Why open source? If there were an API that was closed source and free for use (any way you like), would you reject it? –  Andrew Thompson Commented Dec 2, 2012 at 8:58

You can get a help from Sphinx-4 . Sphinx-4 is a state-of-the-art speech recognition system written entirely in the JavaTM programming language.

• I already tried Sphinx-4 but there are too much problem in gram file. I put numbers from zero to ten, but the Sphinx-4 not catch my number properly –  NikhilK Commented Dec 2, 2012 at 9:13

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