Connectionist Probability Estimation in HMM Speech Recognition (1992)
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| Venue: | IEEE Transactions on Speech and Audio Processing |
| Citations: | 45 - 9 self |
BibTeX
@ARTICLE{Renals92connectionistprobability,
author = {Steve Renals and Nelson Morgan},
title = {Connectionist Probability Estimation in HMM Speech Recognition},
journal = {IEEE Transactions on Speech and Audio Processing},
year = {1992},
volume = {2},
pages = {161--174}
}
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Abstract
This report is concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system, This is achieved through a statistical understanding of connectionist networks as probability estimators, first elucidated by Herve Bourlard. We review the basis of HMM speech recognition, and point out the possible benefits of incorporating connectionist networks. We discuss some issues necessary to the construction of a connectionist HMM recognition system, and describe the performance of such a system, including evaluations on the DARPA database, in collaboration with Mike Cohen and Horacio Franco of SRI International. In conclusion, we show that a connectionist component improves a state of the art HMM system. ii Part I INTRODUCTION Over the past few years, connectionist models have been widely proposed as a potentially powerful approach to speech recognition (e.g. Makino et al. (1983), Huang et al. (1988) and Waibel et al. (1989)). However, whilst connec...
