Soft-decision vector quantization based on the Dempster/Shafer theory

Abstract

The authors describe an algorithm for soft-decision vector quantization (SVQ) implemented in the acoustic front-end of a large-vocabulary speech recognizer based on discrete density HMMs (hidden Markov models) of small phonetic units. In contrast to hard-decision vector quantization (HVQ), the proposed approach transforms a feature vector into a number of symbols associated with credibility values computed according to statistical models of distances and evidential reasoning. SVQ is related to semi-continuous density HMMs (SCHMMs). In contrast to SCHMM, which is based on multidimensional, class-specific distributions of feature vectors, SVQ is based on one-dimensional distributions of distances and is therefore much simpler. Credibilities and associated symbols form the inputs to both the HMM-training and the recognition modules of the system. SVQ improves recognition results remarkably.