Speech recognition using hybrid hidden Markov model and NN classifier

This paper discusses the use of an integrated HMM/NN classifier for speech recognition. The proposed classifier combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN) classifier. Speech signals display a strong time varying characteristic. Although the neural net has been successful in many classification problems, its success (compared to HMM) is secondary to HMM in the field of speech recognition. The main reason is the lack of time normalization characteristics of most neural net structures (time-delay neural net is one notable exception but its structure is very complex). In the proposed integrated hybrid HMM/NN classifier, a left-to-right HMM module is used first to segment the observation sequence of every exemplar into a fixed number of states. Subsequently, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time scale variation, is transformed into a fixed number of frames, i.e., a static pattern. The multilayer perceptron (MLP) neural net is then used as the classifier for these time normalized exemplars. Some experimental results using telephone speech databases are presented to demonstrate the potential of this hybrid integrated classifier. © 1998 Kluwer Academic Publishers.