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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

          Journal
          IEEE Transactions on Pattern Analysis and Machine Intelligence
          IEEE Trans. Pattern Anal. Mach. Intell.
          Institute of Electrical and Electronics Engineers (IEEE)
          0162-8828
          March 1983
          March 1983
          : PAMI-5
          : 2
          : 179-190
          10.1109/TPAMI.1983.4767370
          21869099
          © 1983
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