Abstract Speaker independent recognition of small vocabularies, spoken over the long distance telephone network, is achieved using two types of models, one type for defined vocabulary words (e.g., collect, calling-card, person, third-number and operator), and one type for extraneous input which ranges from non-speech sounds to groups of non-vocabulary words (e.g. `I want to make a collect call please`). For this type of key word spotting, modifications are made to a connected word speech recognition algorithm based on state-transitional (hidden Markov) models which allow it to recognize words from a pre-defined vocabulary list spoken in an unconstrained fashion. Statistical models of both the actual vocabulary words and the extraneous speech and background noises are created. A syntax-driven connected word recognition system is then used to find the best sequence of extraneous input and vocabulary word models for matching the actual input speech.
A method for generating a sink model for use in a speech recognition system, said method comprising the steps of obtaining a first extraneous sound training token, obtaining a second extraneous sound training token, said first and second extraneous sound training tokens being other than repetitions of a particular one vocabulary item, and generating said sink model in response to a plurality of extraneous sound training tokens which includes said first and second extraneous sound training tokens.
Claim 2
Independent Claim
77 words
A method for generating a sink model for use in a speech recognition system, said method comprising the steps of obtaining a first extraneous sound training token, obtaining a second extraneous sound training token, said first and second extraneous sound training tokens being other than repetitions of a particular one vocabulary item, and generating said sink model in response to a plurality of extraneous sound training tokens which includes said first and second extraneous sound training tokens.
Claim 3
Dependent Claim
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The method of claim 1 wherein said first and second extraneous sound training tokens are respective different vocabulary items.
Claim 4
Dependent Claim
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The method of claim 1 wherein said first and second extraneous sound training tokens are a background sound and a vocabulary item, respectively.
Claim 5
Dependent Claim
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The method of claim 3 wherein said background sound includes a silence component.
Claim 6
Independent Claim
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The method of any one of claims 1 through 4 comprising the further step of combining said sink speech recognition model with a plurality of key word speech recognition models into a grammar which defines expected sequences of keywords and extraneous sounds.
Claim 7
Dependent Claim
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The invention of any one of claims 1 through 4 wherein each of said plurality of speech recognition models is a Hidden Markov Model.
Claim 8
Dependent Claim
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A sink model stored in a storage medium generated by the method of any one of claims 1 through 4.