Fuzzy vector quantazation applied to hidden Markov modeling

Abstract

This paper investigates the use of a fuzzy vector quantizer (FVQ) as the front end for a hidden Markov modeling @€"J 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 H" algorithm.

Stochastic Modelling of Temporal Information in Speech With Fuzzy Vector Quantisation

“…Instead of assigning only one codeword to 1,. the [any vector quantiser quantises the acoustic vector to L nearest codewords each of which is associated with a dis--tortion to x,. The degree of authcnt of x, to codeword v, is de ned as [13] L l 1(v.lx. )={Ewan)/d(x..n)lW' } .…”