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A speech recognition grammar is a set of word patterns, and tells a speech recognition system what to expect a human to say. For instance, if you call an auto-attendant application, it will prompt you for the name of a person (with the expectation that your call will be transferred to that person's phone). It will then start up a speech ...
JSGF stands for Java Speech Grammar Format or the JSpeech Grammar Format (in a W3C Note). Developed by Sun Microsystems, it is a textual representation of grammars for use in speech recognition for technologies like XHTML+Voice. JSGF adopts the style and conventions of the Java programming language in addition to use of traditional grammar ...
The Speech Recognition Grammar Specification (SRGS) is used to tell the speech recognizer what sentence patterns it should expect to hear: these patterns are called grammars. Once the speech recognizer determines the most likely sentence it heard, it needs to extract the semantic meaning from that sentence and return it to the VoiceXML interpreter.
Semantic Interpretation for Speech Recognition (SISR) defines the syntax and semantics of annotations to grammar rules in the Speech Recognition Grammar Specification (SRGS). Since 5 April 2007, it is a World Wide Web Consortium recommendation.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
Linear predictive coding (LPC) is a speech coding method used in speaker recognition and speech verification. [citation needed] Ambient noise levels can impede both collections of the initial and subsequent voice samples. Noise reduction algorithms can be employed to improve accuracy, but incorrect application can have the opposite effect.
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
A popular example, often quoted in the field, is the phrase "How to wreck a nice beach", which sounds very similar to "How to recognize speech". [4] As this example shows, proper lexical segmentation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition ...