Parameter re-estimation in semicontinuous hidden Markov modelling of speech with feedback to vector quantisation codebook

摘要

The parameters of the semicontinuous hidden Markov model (SCHMM) can be re-estimated by allowing the codebook to be updated, thus achieving an optimised codebook/model combination. With the optimised codebook, the SCHMM can offer improved recognition accuracy in comparison to both the continuous and the discrete hidden Markov model.