Modeling duration in a hidden Markov model with the exponential family

Authors:
C. D. Mitchell;L. H. Jamieson
Affiliations:
School of Electrical Engineering, Purdue University, West Lafayette, IN;School of Electrical Engineering, Purdue University, West Lafayette, IN
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

Citing 2
Cited 1

A tutorial on hidden Markov models and selected applications in speech recognition

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Continuously variable duration hidden Markov models for automatic speech recognition

Computer Speech and Language

Performing information extraction to improve OCR error detection in semi-structured historical documents

Proceedings of the 2011 Workshop on Historical Document Imaging and Processing

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Abstract

Explicit duration modeling has been shown to increase the effectiveness of hidden Markov models in automatic speech recognition. Ferguson found the optimum parameters of the duration model for the case where duration is assumed to be distributed according to a non-parametric probability mass function. Levinson determined the best gamma density to model duration. In this paper, duration is assumed to be modeled by some probability mass function in the exponential family. An iterative procedure for determining the maximum likelihood parameters is presented. Also given is a method for choosing an appropriate member from the exponential family.