On increasing structural complexity of finite state speech models

Authors:
S. V. Vaseghi;P. Conner
Affiliations:
School of Information Systems, University of East Anglia, Norwich, UK;School of Information Systems, University of East Anglia, Norwich, UK
Venue:
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Year:
1992

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Continuously variable duration hidden Markov models for automatic speech recognition

Computer Speech and Language

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Abstract

The paper explores some new methods of modeling speech spectral features and duration within the frame work of finite state models. On observation modeling, the use of cepstral-time matrices, instead of cepstral vectors, as the observation unit is investigated. On duration modeling a new HMM is introduced in which state transition and duration probabilities are combined to form duration dependent transition probabilities. The duration dependent transitions are derived from the cumulative density function (CDF) of state duration.