Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Sec-ond the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to care-fully and methodically review the theoretical aspects of this type of statistical modeling and show how they have been applied to selected problems in machine recognition of speech. I.
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225
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48
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46
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44
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43
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Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition
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39
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A segmental K-means training procedure for connected word recognition
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31
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28
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16
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2
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2
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1
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1
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1
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1
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1
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1
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1
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1
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Global connected digit recognition using Baum-Welch algorithm
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1
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Context dependent phonetic Markov models for large vocabulary speech recognition
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