Program for speech recognition in java

Furthermore, if the context changes for example, due to a mouse click to move the cursor the application should update the context.

Different recognizers process context differently. The main consideration for the application is the amount of context to provide to the recognizer. As a minimum, a few words of preceding and following context should be provided. However, some recognizers may take advantage of several paragraphs or more. The first form takes plain text context strings.

The second version should be used when the result tokens returned by the recognizer are available. Internally, the recognizer processes context according to tokens so providing tokens makes the use of context more efficient and more reliable because it does not have to guess the tokenization.

A recognition result is provided by a Recognizer to an application when the recognizer "hears" incoming speech that matches an active grammar. The result tells the application what words the user said and provides a range of other useful information, including alternative guesses and audio data. In this section, both the basic and advanced capabilities of the result system in the Java Speech API are described. The sections relevant to basic rule grammar-based applications are those that cover result finalization Section 6.

For dictation applications the relevant sections include those listed above plus the sections covering token finalization Section 6. For more advanced applications relevant sections might include the result life cycle Section 6.

In that example, a RuleGrammar was loaded, committed and enabled, and a ResultListener was attached to a Recognizer to receive events associated with every result that matched that grammar.

In other words, the ResultListener was attached to receive information about words spoken by a user that is heard by the recognizer.

The following is a modified extract of the "Hello World! In this case, a ResultListener is attached to a Grammar instead of a Recognizer and it prints out every thing the recognizer hears that matches that grammar.

There are, in fact, three ways in which a ResultListener can be attached: see Section 6. The ResultAdapter class is a convenience implementation of the ResultListener interface provided in the javax. When extending the ResultAdapter class we simply implement the methods for the events that we care about.

This event is issued to the resultAccepted method of the ResultListener and is issued when a result is finalized. Finalization of a result occurs after a recognizer completed processing of a result. More specifically, finalization occurs when all information about a result has been produced by the recognizer and when the recognizer can guarantee that the information will not change. Result finalization should not be confused with object finalization in the Java programming language in which objects are cleaned up before garbage collection.

A result is accepted when a recognizer is confidently that it has correctly heard the words spoken by a user i. Rejection occurs when a Recognizer is not confident that it has correctly recognized a result: that is, the tokens and other information in the result do not necessarily match what a user said.

Rejected results and the differences between accepted and rejected results are described in more detail in Section 6. An accepted result is not necessarily a correct result. As is pointed out in Section 2. The implication is that even for an accepted result, application developers should consider the potential impact of a misrecognition. Where a misrecognition could cause an action with serious consequences or could make changes that can't be undone e.

As recognition systems continue to improve the number of errors is steadily decreasing, but as with human speech recognition there will always be a chance of a misunderstanding. A finalized result can include a considerable amount of information.

This information is provided through four separate interfaces and through the implementation of these interfaces by a recognition system. At first sight, the result interfaces may seem complex. The reasons for providing several interfaces are as follows:.

The multitude of interfaces is, in fact, designed to simplify application programming and to minimize the chance of introducing bugs into code by allowing compile-time checking of result calls. The two basic principles for calling the result interfaces are the following:.

In the next section the different information available through the different interfaces is described. In all the following sections that deal with result states and result events, details are provided on the appropriate casting of result objects.

As the previous section describes, different information is available for a result depending upon the state of the result and, for finalized results, depending upon the type of grammar it matches RuleGrammar or DictationGrammar. The information available through the Result interface is available for any result in any state - finalized or unfinalized - and matching any grammar. In addition to the information detailed above, the Result interface provides the addResultListener and removeResultListener methods which allow a ResultListener to be attached to and removed from an individual result.

ResultListener attachment is described in more detail in Section 6. The information available through the FinalResult interface is available for any finalized result, including results that match either a RuleGrammar or DictationGrammar. The FinalRuleResult interface also provides some additional information that is useful in processing results that match a RuleGrammar. A Result is produced in response to a user's speech.

Unlike keyboard input, mouse input and most other forms of user input, speech is not instantaneous see Section 6. As a consequence, a speech recognition result is not produced instantaneously. Instead, a Result is produced through a sequence of events starting some time after a user starts speaking and usually finishing some time after the user stops speaking.

Figure shows the state system of a Result and the associated ResultEvents. As in the recognizer state diagram Figure , the blocks represent states, and the labelled arcs represent transitions that are signalled by ResultEvents.

While unfinalized, the recognizer provides information including finalized and unfinalized tokens and the identity of the grammar matched by the result. Once all information associated with a result is finalized, the entire result is finalized.

At that point all information associated with the result becomes available including the best guess tokens and the information provided through the three final result interfaces see Section 6. Once finalized the information available through all the result interfaces is fixed. The only exceptions are for the release of audio data and training data. Applications can track result states in a number of ways. Most often, applications handle result in ResultListener implementation which receives ResultEvents as recognition proceeds.

However, as the example in Section 6. The state of a result is also available through the getResultState method of the Result interface. A ResultListener can be attached in one of three places to receive events associated with results: to a Grammar , to a Recognizer or to an individual Result.

The different places of attachment give an application some flexibility in how they handle results. Depending upon the place of attachment a listener receives events for different results and different subsets of result events. The state system of a recognizer is tied to the processing of a result. While the result finalization event is processed, the recognizer remains suspended.

In many applications, grammar definitions and grammar activation need to be updated in response to spoken input from a user. For example, if speech is added to a traditional email application, the command "save this message" might result in a window being opened in which a mail folder can be selected.

While that window is open, the grammars that control that window need to be activated. Thus during the event processing for the "save this message" command grammars may need be created, updated and enabled. For any grammar changes to take effect they must be committed see Section 6. One desirable effect of this form of commit becomes useful in component systems. If changes in multiple components are triggered by a finalized result event, and if many of those components change grammars, then they do not each need to call the commitChanges method.

The downside of multiple calls to the commitChanges method is that a syntax check be performed upon each. Checking syntax can be computationally expensive and so multiple checks are undesirable.

With the implicit commit once all components have updated grammars computational costs are reduced. This event is issued is issued only once. A result is a dynamic object a it is being recognized. One way in which a result can be dynamic is that tokens are updated and finalized as recognition of speech proceeds.

The result events allow a recognizer to inform an application of changes in the either or both the finalized and unfinalized tokens of a result. Finalized tokens are accessed through the getBestTokens and getBestToken methods of the Result interface.

The unfinalized tokens are accessed through the getUnfinalizedTokens method of the Result interface. See Section 6. A finalized token is a ResultToken in a Result that has been recognized in the incoming speech as matching a grammar. Furthermore, when a recognizer finalizes a token it indicates that it will not change the token at any point in the future.

The numTokens method returns the number of finalized tokens. Many recognizers do not finalize tokens until recognition of an entire result is complete. An unfinalized token is a token that the recognizer has heard, but which it is not yet ready to finalize.

Recognizers are not required to provide unfinalized tokens, and applications can safely choose to ignore unfinalized tokens. For recognizers that provide unfinalized tokens, the following conditions apply:.

Unfinalized tokens are highly changeable, so why are they useful? Many applications can provide users with visual feedback of unfinalized tokens - particularly for dictation results.

This feedback informs users of the progress of the recognition and helps the user to know that something is happening. However, because these tokens may change and are more likely than finalized tokens to be incorrect, the applications should visually distinguish the unfinalized tokens by using a different font, different color or even a different window. The following is an example of finalized tokens and unfinalized tokens for the sentence "I come from Australia".

The finalized tokens are in bold, the unfinalized tokens are in italics. Recognizers can vary in how they support finalized and unfinalized tokens in a number of ways. For an unfinalized result, a recognizer may provide finalized tokens, unfinalized tokens, both or neither. Furthermore, for a recognizer that does support finalized and unfinalized tokens during recognition, the behavior may depend upon the number of active grammars, upon whether the result is for a RuleGrammar or DictationGrammar , upon the length of spoken sentences, and upon other more complex factors.

The are some common design patterns for processing accepted finalized results that match a RuleGrammar. First we review what we know about these results.

This means that the tokenization of the result follows the tokenization of the grammar definition including compound tokens. For example, consider a grammar with the following Java Speech Grammar Format fragment which contains four tokens:. The ResultToken interface defines more advanced information. Amongst that information the getStartTime and getEndTime methods may optionally return time-stamp values or -1 if the recognizer does not provide time-alignment information.

The ResultToken interface also defines several methods for a recognizer to provide presentation hints. Furthermore, the getSpokenText and getWrittenText methods will return an identical string which is equal to the string defined in the matched grammar. In a FinalRuleResult , alternative guesses are alternatives for the entire result, that is, for a complete utterance spoken by a user. A FinalDictationResult can provide alternatives for single tokens or sequences of tokens.

Because more than one RuleGrammar can be active at a time, an alternative token sequence may match a rule in a different RuleGrammar than the best guess tokens, or may match a different rule in the same RuleGrammar as the best guess.

Thus, when processing alternatives for a FinalRuleResult , an application should use the getRuleGrammar and getRuleName methods to ensure that they analyze the alternatives correctly. Alternatives are numbered from zero up. The 0th alternative is actually the best guess for the result so FinalRuleResult. The duplication is for programming convenience. Likewise, the FinalRuleResult. The implementation assumes that a Result being processed matches a RuleGrammar. For a grammar with commands to control a windowing system shown below , a result might look like:.

Processing commands generated from a RuleGrammar becomes increasingly difficult as the complexity of the grammar rises. With the Java Speech API, speech recognizers provide two mechanisms to simplify the processing of results: tags and parsing. A tag is a label attached to an entity within a RuleGrammar.

The following is a grammar for very simple control of windows which includes tags attached to the important words in the grammar. The italicized words are the ones that are tagged in the grammar - these are the words that the application cares about. For example, in the third and fourth example commands, the spoken words are different but the tagged words are identical.

Tags allow an application to ignore trivial words such as "the" and "to". The com. The tags for the best result are available through the getTags method of the FinalRuleResult interface. This method returns an array of tags associated with the tokens words and other grammar entities matched by the result. If the best sequence of tokens is "move the window to the front", the list of tags is the following String array:. Note how the order of the tags in the result is preserved forward in time.

These tags are easier for most applications to interpret than the original text of what the user said. Tags can also be used to handle synonyms - multiple ways of saying the same thing.

For example, "programmer", "hacker", "application developer" and "computer dude" could all be given the same tag, say "DEV".

An application that looks at the "DEV" tag will not care which way the user spoke the title. Another use of tags is for internationalization of applications. Maintaining applications for multiple languages and locales is easier if the code is insensitive to the language being used. In the same way that the "DEV" tag isolated an application from different ways of saying "programmer", tags can be used to provide an application with similar input irrespective of the language being recognized.

The following is a grammar for French with the same functionality as the grammar for English shown above. For this simple grammar, there are only minor differences in the structure of the grammar e. However, in more complex grammars the syntactic differences between languages become significant and tags provide a clearer improvement. Tags do not completely solve internationalization problems.

One issue to be considered is word ordering. A simple command like "open the window" can translate to the form "the window open" in some languages. More complex sentences can have more complex transformations. Thus, applications need to be aware of word ordering, and thus tag ordering when developing international applications. More advanced applications parse results to get even more information than is available with tags.

Parsing is the capability to analyze how a sequence of tokens matches a RuleGrammar. Parsing of text against a RuleGrammar is discussed in Section 6. However, the FinalRuleResult provides tag information for only the best-guess result, whereas parsing can be applied to the alternative guesses. This is not guaranteed, however, if the result was rejected or if the RuleGrammar has been modified since it was committed and produced the result.

The are some common design patterns for processing accepted finalized results that match a DictationGrammar. The ResultTokens provided in a FinalDictationResult contain specialized information that includes hints on textual presentation of tokens. In this section the methods for obtaining and using alternative tokens are described.

Alternative tokens for a dictation result are most often used by an application for display to users for correction of dictated text.

A typical scenario is that a user speaks some text - perhaps a few words, a few sentences, a few paragraphs or more. Previous version of Sphinx we used to write dictionary to recognize. But now we have tokens ready with Sphinx so we give it to Configuration Object. Hope this post will help, please give me feedback. Assuming Maven project. 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.

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. 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. Types of Computer Networks. Classical Synchronization Problem.

What are Semaphores? It is super easy to recognize speech in a browser using JavaScript and then getting the text from the speech to use as user input. We have already covered How to convert Text to Speech in Javascript.

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

This tutorial will cover a basic example where we will cover speech to text. We will ask the user to speak something and we will use the SpeechRecognition object to convert the speech into text and then display the text on the screen. We can provide a list of rules for words or sentences as grammar using the SpeechGrammarList object, which will be used to recognize and validate user input from speech.

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

We can use grammar, to define rules for speech recognition, configuring what our app understands and what it doesn't understand. In the code example below, we will use the SpeechRecognition object. We haven't used too many properties and are relying on the default values. We have a simple HTML webpage in the example, where we have a button to initiate the speech recognition. The main JavaScript code which is listening to what user speaks and then converting it to text is this:.

Once we begin speech recognition, the onstart event handler can be used to inform the user that speech recognition has started and they should speak into the mocrophone. When the user is done speaking, the onresult event handler will have the result. It has a getter so it can be accessed like an array.

The first [0] returns the SpeechRecognitionResult at the last position. These also have getters so they can be accessed like arrays.