Modelling and processing filled pauses and noises in speech recognition
First Claim
1. A computer readable media including instructions readable by a computing device which, when implemented, cause the computing device to handle information by performing steps comprising:
- receiving input speech;
accessing information indicative of a lexicon including entries transcribed by phones, an acoustic model modeling phones for entries in the lexicon, the acoustic model including at least one ergodic Hidden Markov Model (HMM) model for a filled pause, and a language model modeling word order with filled pauses; and
processing the input speech using the information to identify filled pauses in the input speech.
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Abstract
A speech recognition system recognizes filled pause utterances made by a speaker. In one embodiment, an ergodic model is used to acoustically model filled pauses that provides flexibility allowing varying utterances of the filled pauses to be made. The ergodic HMM model can also be used for other types of noise such as but limited to breathing, keyboard operation, microphone noise, laughter, door openings and/or closings, or any other noise occurring in the environment of the user or made by the user. Similarly, silence can be modeled using an ergodic HMM model. Recognition can be used with N-gram, context-free grammar or hybrid language models.
35 Citations
22 Claims
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1. A computer readable media including instructions readable by a computing device which, when implemented, cause the computing device to handle information by performing steps comprising:
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receiving input speech;
accessing information indicative of a lexicon including entries transcribed by phones, an acoustic model modeling phones for entries in the lexicon, the acoustic model including at least one ergodic Hidden Markov Model (HMM) model for a filled pause, and a language model modeling word order with filled pauses; and
processing the input speech using the information to identify filled pauses in the input speech. - View Dependent Claims (2, 3, 4, 5)
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6. A computer-implemented method of forming a context-free grammar language model for use in speech recognition, the method comprising:
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accessing a set of context-free grammar rules; and
creating a context-free grammar language model as a function of the context-free grammar rules, wherein portions of the model for at least some of the rules include allowing identification for filled pauses to occur at a position corresponding to beginning nodes of said at least some the context-free grammar rules. - View Dependent Claims (7, 8)
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9. A speech recognition system, comprising:
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an input receiving a signal indicative of speech;
a lexicon including entries transcribed by phones;
an acoustic model modeling phones for entries in the lexicon, the acoustic model including at least one ergodic Hidden Markov Model (HMM) model for a filled pause;
a language model modeling word order with filled pauses; and
a decoder coupled to the input, the acoustic model and the language model, recognizing speech including filled pauses represented by the signal. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification