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. © 2002 Elsevier Science B.V. All rights reserved.
Bibliographical noteThis work was supported by the City University of Hong Kong Strategic Grant 7001059 and the Natural Science Foundation Funds of China.
- Genetic algorithms
- Global optimization
- Minimum classification error
- Speech processing