Maximum mutual information codebook mapping for discrete hidden Markov models

This paper presents a new algorithm of supervised vector quantization based on codebook mapping and maximization of mutual information. Classical VQ algorithms are usually based on minimization of a distortion criterion and hence, the phonetic classification of the acoustic vectors is not taken into account during the design of the codebooks. Moreover, the regions defined by classical VQ algorithms are generally limited to Voronoi regions. We show how the MMI mapping can design more complex class regions while taking the phonetic information associated to the input vectors into account. Recognition experiments have been conducted on an isolated word recognition task. These experiments show that the MMI mapping outperforms and is more robust to test conditions than classical VQ algorithms