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A maximum likelihood approach to continuous speech recognition.

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      Abstract

      Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them.

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      Author and article information

      Affiliations
      [1 ] MEMBER, IEEE, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598.
      Journal
      IEEE Trans Pattern Anal Mach Intell
      IEEE transactions on pattern analysis and machine intelligence
      0162-8828
      0098-5589
      Feb 1983
      : 5
      : 2
      21869099

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