An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition
S. Levinson, L. R. Rabiner, M. M. Sondhi · Bell System Technical Journal · 1983
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.