HMM based on pair-wise Bayes classifiers

A novel hidden Markov model (HMM) architecture which realizes both high discriminating ability and stochastic scoring is presented. In modifying continuous HMM so that the states of the models are best separated, distinctive features vary for different states or different models, and different discriminant functions should be made for different competing states. For every pair of the states, a Bayes classifier which performs a vector transformation based on discriminant analysis is constructed. Each classifier ranks the two states and computes a relative value of the probabilities. Output probabilities of the HMM states are obtained by combining and normalizing the results of the pair-wise classifications. Training of the classifiers and HMMs is done interactively and iteratively so that they are optimized totally. Experimental results show that the method, called the pair-wise Bayes classifier-HMM (PWBC-HMM) is more effective than the conventional HMM. It realizes robust recognition by modifying pattern space to fully separate confusing classes, while retaining analog outputs by statistical Bayes classifiers