Corrective tuning by applying LVQ for continuous density and semi-continuous Markov models

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%