Fuzzy vector quantazation applied to hidden Markov modeling

This paper investigates the use of a fuzzy vector quantizer (FVQ) as the front end for a hidden Markov modeling (HMM) scheme for isolated word recognition. Unlike a standard vector quantizer that generates the index of a single codeword that best matches an input vector, an FVQ generates a vector whose components represent the degree to which each codeword matches the input vector. The HMM algorithm is generalized to accommodate the FVQ output. This approach is tested on a database of isolated words from a single male speaker. It is seen that the FVQ front end significantly reduces the amount of data needed to train the HMM algorithm.