New state clustering of hidden Markov network with Korean phonological rules for speech recognition

In this paper, we adopted the Korean phonological rules to state clustering of contextual domain for representing the unknown contexts and tying the model parameters of new states in state clustering of SSS (Successive State Splitting). We used the Decision Tree-based Successive State Splitting (DT-SSS) algorithm, which splits the state of contexts based on phonetic knowledge. The SSS algorithm proposed by Sagayama is a powerful technique, which designed topologies of tied-state HMMs automatically, but it doesnt generate unknown contexts adequately. In addition it has some problem in the contextual splits procedure. In this paper, the speaker independent Korean isolated word and sentence recognition experiments are carried out. In word recognition experiments, this method shows an average of 6.3% higher word recognition accuracy than the conventional HMMs. And in sentence recognition experiments, it shows an average of 90.9% recognition accuracy.