Abstract
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.
Keywords
Emphasis (telecommunications)Class (philosophy)Probabilistic logicComputer scienceHidden Markov modelSalientMarkov chainWord (group theory)Process (computing)Speech recognitionArtificial intelligenceMarkov modelMarkov processAlgorithmPattern recognition (psychology)Machine learningMathematics
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Publication Info
- Year
- 1983
- Type
- article
- Volume
- 62
- Issue
- 4
- Pages
- 1035-1074
- Citations
- 979
- Access
- Closed
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Cite This
S. Levinson,
L. R. Rabiner,
M. M. Sondhi
(1983).
An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition.
Bell System Technical Journal
, 62
(4)
, 1035-1074.
https://doi.org/10.1002/j.1538-7305.1983.tb03114.x
Identifiers
- DOI
- 10.1002/j.1538-7305.1983.tb03114.x