Java Speech Development Kit: A Tutorial
Java Speech Markup Language (JSML) is a markup language derived from XML and is used to describe an audio entrance in the synthesizers of the API. Its elements are able to supply detailed information about how to pronounce the text provided to the synthesizer. These elements describe the structure of a document (paragraphs, phrases), the words pronounced, text markers and prosodic elements that control phrasing, emphasis, pitch, speaking rate and other important characteristics. Appropriate markup of text improves the quality and naturalness of the synthesized voice.
JSML is used as arguments to the synthesizers and can be accessed through one of the forms of the method speak. It might be provided in a text file with the JSML formatted text or in small phrases as arguments for the method. Table 4 shows the JSML tags:
Table 2. JSML Tags
Determines that the marked text must be pronounced as a paragraph.
<PARA>This is a short paragraph.</PARA> <PARA>The subject has changed, so this is a new paragraph.</PARA>
Determines that the marked text must be pronounced as a phrase.
<PARA><SENT>So long.</SENT><SENT>See you later.</SENT></PARA>
Specifies how the marked text must be read.
<SAYAS SUB="0x0">zero draw</SAYAS>
Determines the emphasis of the pronounce. The values might be: "strong", "moderate", "none", or "reduced".
<EMP LEVEL="reduced">Whispering is not polite.</EMP>
Determines a stop during synthesis. Milliseconds or size can be used: "none", "small", "medium", or "large".
<BREAK MSECS="300"/> or <BREAK SIZE="small"/>
Determines the speech properties: volume (VOL absolute number between 0.0 and 1.0), rhythm (RATE in words per minute), frequency (PITCH in hertz), frequency interval (RANGE in hertz).
<PROS RATE= "50" VOL="+80%">Í'll repeat loud and clear, just one more time. </PROS>
When this tag is reached a markerReached event is triggered, this might be interpreted by a SpeakableListener. A tag MARKER can be positioned anywhere in the JSML text, making it possible to monitor the whole text being pronounced.
Answer <MARKER MARK="yes_no"/> yes or no.
Provides information to the synthesizer identified by the ENGID, that will pronounce what is defined by the DATA parameter. If the synthesizer is not available, the marked text will be pronounced instead.
The frog does <ENGINE ENGID= "Frog_Sint1.0" DATA="ribbit=1"> improper synthesizer</ENGINE>
The recognizers are the entities responsible for speech recognition. They are created through the Central class and are initialized using the allocate method. They process the audio entering in parallel to the main application.
The recognition is done with the help of a set of words that limit the universe of possibilities that can be pronounced by the user. These sets are extensible, and new words can be added, increasing the application functionality. The sets are known as grammars, and it is necessary to have at least one grammar associated with each recognizer.
When audio enters the recognizers, they try to determine an association between the spoken words and their associated grammars. The recognition process culminates on the creation of objects named results. Results have types ACCEPTED or REJECTED accordingly to the confidence level determined by the setConfidenceLevel method (the default value is 0.5).
In the same way as the synthesizers, the recognizers are created through the Central class in two possible ways:
1. Accessing the recognizer's default implementation that better fits the current locale. It is the simplest manner and the most common:
Locale.setDefault("en","US"); Recognizer recognizer = Central.createRocognizer(null);
2. Accessing a recognizer that satisfies predetermined conditions, this method is used in cases where there are more than one recognizer implementation available. The required parameters are:
name of the engine
name of the mode in which it will be used
a locale supported by the engine
a Boolean value, a control flag of the engine
a Boolean value, indicating whether the recognizer supports dictation grammar
an array of SpeakerProfile objects
Any of the parameters might be null, and the Central class will try to make the best choice for them. These parameters are defined via a RecognizerModeDesc object, as shown here:
RecognizerModeDesc(String engine, String mode, Locale locale, Boolean running, Boolean dictationGrammarSupported, SpeakerProfile profiles)
|Designing Electronics with Linux||May 22, 2013|
|Dynamic DNS—an Object Lesson in Problem Solving||May 21, 2013|
|Using Salt Stack and Vagrant for Drupal Development||May 20, 2013|
|Making Linux and Android Get Along (It's Not as Hard as It Sounds)||May 16, 2013|
|Drupal Is a Framework: Why Everyone Needs to Understand This||May 15, 2013|
|Home, My Backup Data Center||May 13, 2013|
- Designing Electronics with Linux
- Making Linux and Android Get Along (It's Not as Hard as It Sounds)
- Dynamic DNS—an Object Lesson in Problem Solving
- Using Salt Stack and Vagrant for Drupal Development
- Build a Skype Server for Your Home Phone System
- Why Python?
- New Products
- A Topic for Discussion - Open Source Feature-Richness?
- Validate an E-Mail Address with PHP, the Right Way
- Tech Tip: Really Simple HTTP Server with Python
- Understanding the Linux Kernel
1 hour 19 min ago
3 hours 49 min ago
- Kernel Problem
13 hours 52 min ago
- BASH script to log IPs on public web server
18 hours 19 min ago
21 hours 54 min ago
- Reply to comment | Linux Journal
22 hours 27 min ago
- All the articles you talked
1 day 50 min ago
- All the articles you talked
1 day 54 min ago
- All the articles you talked
1 day 55 min ago
1 day 5 hours ago
Enter to Win an Adafruit Pi Cobbler Breakout Kit for Raspberry Pi
It's Raspberry Pi month at Linux Journal. Each week in May, Adafruit will be giving away a Pi-related prize to a lucky, randomly drawn LJ reader. Winners will be announced weekly.
Fill out the fields below to enter to win this week's prize-- a Pi Cobbler Breakout Kit for Raspberry Pi.
Congratulations to our winners so far:
- 5-8-13, Pi Starter Pack: Jack Davis
- 5-15-13, Pi Model B 512MB RAM: Patrick Dunn
- 5-21-13, Prototyping Pi Plate Kit: Philip Kirby
- Next winner announced on 5-27-13!
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?