Phoneme recognition by combining discriminant analysis and HMM

The authors present a novel phoneme recognition method which combines two stochastic methods; discriminant analysis and the hidden Markov model (HMM) method. The HMM is powerful in time-warping and in capturing the global dynamic features, but its discriminating ability is not sufficient. The approach used is to construct the HMM with a phonetic element classifier front-end. Each phonetic element belongs to one phoneme and represents a local pattern of the phoneme. The classifier is a modified version of discriminant analysis, that is, a combination of Bayes classifiers. It extracts optimal features to separate the phonetic elements and consequently contributes to separate HMMs of the phonemes. Furthermore, the score of the classifier is combined with that of the HMM. Since the classifier is based on a statistical method, combination of the scores is straightforward both in theory and in practice. Experimental results showed that the combined method is more effective than the conventional VQ (vector quantization) HMM and that utilizing the score of the classifier on the local features is significant