The theory of hidden Markov models and its application to speech recognition.

Stochastic methods of signal modeling have become increasingly popular. There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to carefully and methodically review the theoretical aspects of one type of stochastic modelling, namely hidden Markov models (HMM's) and show how they have been applied to a couple of problems in machine recognition of speech.