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Computing the observed information in the hidden Markov model using the EM algorithm

โœ Scribed by James P. Hughes

Book ID
104302446
Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
409 KB
Volume
32
Category
Article
ISSN
0167-7152

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โœฆ Synopsis

A method of computing the observed information for the hidden Markov model using the EM algorithm and the results of Louis ( ) is described. Generating the "exact" information may be computationally intensive for large datasets but an approximation is given which significantly reduces the computational effort in most cases.

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For hidden Markov model (HMM) based speech recognition where the basic speech unit is smaller than the recognizer's output unit, the standard full Baum-Welch re-estimation procedure for the HMM training is very costly in computation. This is hecause it requires evaluation of the HMM output densities