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Hidden Markov model speech recognition arrangement

  • US 4,587,670 A
  • Filed: 10/15/1982
  • Issued: 05/06/1986
  • Est. Priority Date: 10/15/1982
  • Status: Expired due to Term
First Claim
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1. A speech analyzer for recognizing an utterance as one of a plurality of reference patterns each having a frame sequence of acoustic feature signals comprising:

  • means for storing a set of K signals each representative of a prescribed acoustic feature of said plurality of reference patterns;

    means for storing a plurality of templates each representative of an identified spoken reference pattern, the template of each spoken reference pattern comprising signals representative of a first state, a last state and a preselected number N-2 intermediate states between said first and last states of a constrained hidden Markov model of said spoken reference pattern, N being independent of the number of acoustic feature frames in the acoustic feature frame sequence of the identified spoken reference pattern, a plurality of first type signals each representative of the likelihood of a prescribed acoustic feature signal of a reference pattern frame being in a predetermined one of said states, and a plurality of second type signals each representative of the likelihood of a transition from a prescribed acoustic feature signal in one of said states to another of said states of said template;

    means responsive to the utterance for forming a time frame sequence of acoustic feature signals representative of the speech pattern of the utterance;

    means responsive to said utterance feature signal sequence and said stored prescribed acoustic feature signals for selecting a sequence of said prescribed feature signals representative of the utterance speech pattern;

    means jointly responsive to said sequence of prescribed feature signals representative of the utterance and the reference pattern template N state constrained hidden Markov model signals for combining said utterance representative sequence of prescribed feature signal sequence with said reference pattern N state Markov model template signals to form a third type signal representative of the likelihood of the unknown utterance being the spoken reference pattern; and

    means responsive to the third type signals for the plurality of reference patterns for generating a signal to identify the utterance as one of the plurality of reference patterns.

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