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Optimising hidden Markov models using discriminative output distributions

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2 Author(s)
Woodland, P.C. ; Dept. of Eng., Cambridge Univ., UK ; Cole, D.R.

Models similar to Doddington's (1989, 1990) hidden Markov models (HMMs) that use phonetically sensitive discriminants are discussed. In this style of HMM, each state models a subspace of the overall acoustic vector; the subspace is chosen to increase discrimination between the in-class and potentially confusable out-of-class utterances. The theoretical basis is presented and various aspects of using these models are discussed, such as the method of gathering confusion statistics; obtaining the correct normalization for the subspace Gaussian distribution and the effects of this term; and the computational requirements for the method. A large number of experiments on a 104 talker British English E-set database were performed that illustrate the utility of the method on a difficult speech recognition task. The experiments give a best speaker-independent error rate 7.9%, and a best multiple speaker error rate of 3.8%

Published in:

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Date of Conference:

14-17 Apr 1991

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