Creation and use of application-generic class-based statistical language models for automatic speech recognition
First Claim
1. A method comprising:
- accessing a corpus of terms by a parser using a processor in each of a plurality of speech applications;
parsing, using said parser and said processor, said corpus of terms in each speech application to produce a plurality of first output sets, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application, wherein said grammar tags are selected from among command grammar tags and collection grammar tags;
accessing said plurality of first output sets by a class-relabeler and said processor;
replacing by the class-relabeler and said processor, for each of the plurality of speech applications, each of the grammar tags in the plurality of first output sets with a class identifier of an application-generic class, to produce plurality of a second output sets;
accessing said plurality of second output sets by a token selector and said processor;
processing collectively, by said token selector and said processor, the plurality of second output sets or data derived from the output sets with a statistical language model (SLM) trainer; and
generating, using said processor, an application-generic class-based SLM using a set of results from said SLM trainer.
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Abstract
A method of creating an application-generic class-based SLM includes, for each of a plurality of speech applications, parsing a corpus of utterance transcriptions to produce a first output set, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application. The method further includes, for each of the plurality of speech applications, replacing each of the grammar tags in the first output set with a class identifier of an application-generic class, to produce a second output set. The method further includes processing the resulting second output sets with a statistical language model (SLM) trainer to generate an application-generic class-based SLM.
35 Citations
22 Claims
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1. A method comprising:
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accessing a corpus of terms by a parser using a processor in each of a plurality of speech applications; parsing, using said parser and said processor, said corpus of terms in each speech application to produce a plurality of first output sets, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application, wherein said grammar tags are selected from among command grammar tags and collection grammar tags; accessing said plurality of first output sets by a class-relabeler and said processor; replacing by the class-relabeler and said processor, for each of the plurality of speech applications, each of the grammar tags in the plurality of first output sets with a class identifier of an application-generic class, to produce plurality of a second output sets; accessing said plurality of second output sets by a token selector and said processor; processing collectively, by said token selector and said processor, the plurality of second output sets or data derived from the output sets with a statistical language model (SLM) trainer; and generating, using said processor, an application-generic class-based SLM using a set of results from said SLM trainer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of creating a statistical language model (SLM) for automatic speech recognition (ASR), the method comprising:
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for each of a plurality of speech applications, parsing a corpus of utterance transcriptions, with a parser and a processor, from the application to produce a first output set, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application wherein said grammar tags are selected from among command grammar tags and collection grammar tags, wherein said parsing includes; for each identified expression, identifying, with said parser and said processor, a type of grammar to which the expression corresponds, including determining whether the expression corresponds to a first grammar or a second grammar, and selecting, with said parser, a grammar tag to replace the expression based on the identified type of grammar; for each of the plurality of speech applications, replacing, with a class relabeler and said processor, each of the grammar tags in the first output set with a class identifier of an application-generic class, to produce a second output set, including; replacing, with said class-relabeler and said processor, the grammar tag with a first class identifier if the grammar tag is determined to correspond to a grammar of the first type, and replacing, with said class-relabeler and said processor, the grammar tag with a second class identifier if the grammar tag is determined to a grammar of the second type; filtering, with a token selector and said processor, the second output sets collectively based on an algorithm to produce a third output set; and processing the third output set with an SLM trainer and said processor; and generating an application-generic class-based SLM for ASR using a set of results from said SLM trainer and said processor, wherein the application-generic class-based SLM includes one or more of said class identifiers. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A method comprising:
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accessing, using a processor, a corpus of terms by a parser in each of a plurality of speech applications; parsing, using said parser and said processor, said corpus of terms in each speech application to produce a plurality of first output sets, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application, wherein said grammar tags are selected from among command grammar tags and collection grammar tags; accessing, using said processor, said plurality of first output sets by a class-relabeler; replacing, using said processor, by the class-relabeler, for each of the plurality of speech applications, each of the grammar tags in the plurality of first output sets with a class identifier of an application-generic class, to produce plurality of a second output sets; accessing, using said processor, said plurality of second output sets by a token selector; and processing, using said processor, collectively, by said token selector, the plurality of second output sets or data derived from the output sets with a statistical language model (SLM) trainer; generating, using said processor, an application-generic class-based SLM using a set of results from said SLM trainer; and creating, using said processor, an application-specific SLM for use in automatic speech recognition for a target speech application, by incorporating into the application generic class-based SLM an application-specific grammar for the target speech application. - View Dependent Claims (21, 22)
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Specification