MULTIPLE VQ HIDDEN MARKOV MODELING FOR SPEECH RECOGNITION
In this paper a new variant of HMM, named Multiple VQ HMM (MVQHMM), is presented. Its main characteristic is the use of a separate codebook for each model. Procedures for training and probability evaluation of these models are described. The evaluation procedure combines the quantization distortions of the vector sequences with the discrete HMM generation probabilities. Comparative results on an isolated word recognition system are shown, between MVQHMM and discrete and semi-continuous HMM. These results show that using separate codebooks and including the quantization distortion in the decision criterion improve the performance of the system. Furthermore, the multiple VQ hidden Markov models seem to be more robust than the discrete and semi-continuous ones in relation to the inter-speaker variability of the recognition system.
hidden markov models speech recognition