Speaker adaptation using semi-continuous hidden Markov models

Presents a new approach to speaker adaptation based on semi-continuous hidden Markov models (SCHMM). The authors introduce a modification of the semi-continuous codebook updating which allows rapid speaker adaptation. The approach is based on the idea that phonetic information already incorporated in a trained model should be used to update the codebook. Thus the different acoustic representation of a new speaker is learned while the connection between codebook entries and model states remains the same. Several experiments were carried out with a small speech sample. It is possible to demonstrate that the new codebook updating performs better than conventional SCHMM codebook updating and that using a speech sample comprising about 40 seconds of adaptation speech is enough to achieve 50 percent of the difference in performance between full speaker-dependent training and no adaptation at all