A genetic classification error method for speech recognition

Authors:
S. Kwong;Q. H. He;K. W. Ku;T. M. Chan;K. F. Man;K. S. Tang
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, 83 Tatchee Ave., Kowloon, Hong Kong, China;South China University of Technology, Guangzhou, China;Department of Electronic Engineering, City University of Hong Kong, 83 Tatchee Ave., Kowloon, Hong Kong, China;Department of Electronic Engineering, City University of Hong Kong, 83 Tatchee Ave., Kowloon, Hong Kong, China;Department of Electronic Engineering, City University of Hong Kong, 83 Tatchee Ave., Kowloon, Hong Kong, China;Department of Electronic Engineering, City University of Hong Kong, 83 Tatchee Ave., Kowloon, Hong Kong, China
Venue:
Signal Processing
Year:
2002

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Abstract

In this paper, we present a genetic approach for training hidden Markov models using minimum classification error (MCE) as the reestimation criteria. This approach is discriminative and proved to be better than other non-discriminative approach such as the maximum likelihood (ML) method. The major problem of using the MCE is to formulate the error rate estimate as a smooth continuous loss function such that the gradient search techniques can be applied to search for the solutions. A genetic approach for this particular classification error method aimed at finding the global solution or better optimal solutions is proposed. Comparing our approach with the ML and MCE approaches, the experimental results showed that it is superior to both the MCE and ML methods.