Corrective tuning by applying LVQ for continuous density and semi-continuous Markov models
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
In this work the objective is to increase the accuracy of speaker dependent phonetic transcription of spoken utterances using continuous density and semi-continuous HMMs. Experiments with LVQ based corrective tuning indicate that the average recognition error rate can be made to decrease about 5%-10%. Experiments are also made to increase the efficiency of the Viterbi decoding by a discriminative approximation of the output probabilities of the states in the Markov models. Using only a few nearest components of the mixture density functions instead of every component decreases both the recognition error rate (5%-10% for CDHMMs) and the execution time (about 50% for SCHMMs). The lowest average error rates achieved were about 5.6%.Keywords
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