Comparison of discrete and continuous classifier-based HMM

Abstract

To improve discriminating ability of Hidden Markov Model (HMM), we have proposed to incorporate a classifier into HMM. In this paper, we make a comparative study of its discrete distribution version and continuous one. The classifier in discrete model discriminates the symbols that are passed to HMM, whereas the classifier in continuous model discriminates the HMM states and computes their output probabilities as classi-fication scores. Thus, the output probability in discrete model indicates the frequency of the symbol occurrence, while that in continuous model shows the reliability of the classification for a given input. We made experimental evaluation of the both types of HMM with the same classifier, changing its output characteristics. In phoneme recognition, discrete model was superior to continuous one. In word and sentence recognition, however, we found that really stochastic distribution of the output probabilities was significant regardless of the types of HMM.