Soft-decision vector quantization based on the Dempster/Shafer theory
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 665-668 vol. 1
- https://doi.org/10.1109/icassp.1991.150427
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.Keywords
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