Contextual vector quantization for speech recognition with discrete hidden Markov model

By using formulation of the finite mixture distribution identification, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov random field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The VQ schemes studied include the LBG VQ, the classification maximum likelihood (CML) approach, the mixture maximum likelihood (MML) procedure, the ergodic large HMM (LHMM) and the contextual VQ (CVQ) method. The motivation to use the MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition