Using multiple vector quantization and semicontinuous hidden Markov models for speech recognition

Although the continuous HMM (CHMM) technique seems to be the most flexible and complete tool for speech modeling, it is not always used for the implementation of speech recognition systems due to several problems related to training and computational complexity. Besides, it is not clear the superiority of continuous models over other well-known types of HMMs, such as discrete (DHMM) or semicontinuous (SCHMM) models, or multiple vector quantization (MVQ) models, a new type of HMM modeling. The authors propose a new variant of HMM models, the SCMVQ, HMM models (semicontinuous multiple vector quantization HMM), that uses one VQ codebook per recognition unit and several quantization candidates, Formally, SCMVQ modeling is the closest one to CHMM, although requiring less computation than SCHMMs. Besides, the authors show that SCMVQs can obtain better recognition results than DHMMs, SCHMMs or MVQs