In this paper we present several of the salient theoretical and practical issues associated with modeling a speech signal as a probabilistic function of a (hidden) Markov chain. First we give a concise review of the literature with emphasis on the Baum‐Welch algorithm. This is followed by a detailed discussion of three issues not treated in the literature: alternatives to the Baum‐Welch algorithm; critical facets of the implementation of the algorithms, with emphasis on their numerical properties; and behavior of Markov models on certain artificial but realistic problems. Special attention is given to a particular class of Markov models, which we call “left‐to‐right” models. This class of models is especially appropriate for isolated word recognition. The results of the application of these methods to an isolated word, speaker‐independent speech recognition experiment are given in a companion paper.
|Original language||English (US)|
|Number of pages||40|
|Journal||Bell System Technical Journal|
|State||Published - Apr 1983|
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