Links Between Markov Models and Multilayer Perceptrons

TitleLinks Between Markov Models and Multilayer Perceptrons
Publication TypeTechnical Report
Year of Publication1988
AuthorsBourlard, H., & Wellekens C.
Other Numbers494

Hidden Markov models are widely used for automatic speech recognition. They inherently incorporate the sequential character of speech signal and are statistically trained. However, the a priori choice of a model topology limits the flexibility of the HMM's. Another drawback of these models is their weak discriminating power. Multilayer perceptrons are now promising tools in the connectionist approach for classification problems and have already been successfully tested on speech recognition problems. However, the sequential nature of the speech signal remains difficult to handle in that kind of machine. In this paper, a discriminant hidden Markov model is defined and it is shown how a particular multilayer perceptron with contextual and extra feedback input units can be considered as a general form of such Markov models. Relations with other recurrent networks commonly used in speech recognition are also pointed out.

Bibliographic Notes

ICSI Technical Report TR-88-008

Abbreviated Authors

H. Bourlard and C. J. Wellekens

ICSI Publication Type

Technical Report