Speaker adaptation for demi-syllable based continuous density HMM

A novel speaker adaptation method for a speech recognition system which uses a continuous density HMM (hidden Markov model) is proposed. It is a supervised adaptation method in which the HMM parameters are modified for new speakers. It is effective not only for recognition units for which there are training samples available, but also for recognition units for which there are no training samples, since the parameters for these units without training samples are estimated by an interpolation technique which are often used in unsupervised adaptation. The effectiveness of the proposed method was evaluated by large vocabulary word recognition experiments, which were carried out under a demi-syllable-based speaker-dependent speech recognition system. The proposed method is shown to be effective when applied to a speaker independent system, under which the recognition accuracy improved by an average of 2.9% for 50 words of training data