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Speech recognition method using a two-pass search

  • US 5,515,475 A
  • Filed: 06/24/1993
  • Issued: 05/07/1996
  • Est. Priority Date: 06/24/1993
  • Status: Expired due to Term
First Claim
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1. A speech recognition method comprising the stepsgenerating a first set of allophone models for use with acoustic parameter vectors of a first type;

  • generating a second set of allophone models for use with acoustic parameter vectors of a second type;

    providing a network representing a recognition vocabulary, wherein each branch of the network is one of the allophone models and each complete path through the network is a sequence of models representing a word in the recognition vocabulary;

    analyzing an unknown utterance to generate a frame sequence of acoustic parameter vectors for each of the first and second types of acoustic parameter vectors;

    generating a reduced trellis for determining a path through the network having a highest likelihood;

    computing model distances for each frame of acoustic parameter vectors of the first type for all allophone models of the first set;

    finding a maximum model distance for each model of the first set;

    updating the reduced trellis for every frame assuming each allophone model is one-state model with a minimum duration of two frames and a transition probability equal to its maximum model distance;

    sorting end values from the reduced trellis of each path through the vocabulary network;

    choosing a first plurality of candidates for recognition having the highest end values;

    rescoring the first plurality of candidates using a full viterbi method trellis corresponding to the vocabulary network with the model distances computed for the first set of allophone models;

    sorting candidates by score in descending order;

    choosing a second plurality of candidates smaller than the first plurality from the first plurality, for further rescoring using the second set of allophone models and second type of acoustic parameter vectors;

    finding allophone segmentation using the first type of acoustic parameter vectors to identify frames for model distance computations for the second type of acoustic parameter vectors;

    computing model distances for the frames of acoustic parameter vectors of the second type identified for the allophone models of the second set found in the second plurality of candidates;

    rescoring the second plurality of candidates using the Viterbi method with the model distances computed for the allophone models of the second set; and

    comparing the second plurality of candidates'"'"' scores for acoustic parameter vectors of first and second types to select a recognition candidate.

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