Speaker adaptation for a hidden Markov model

During the training process, parameters of an HMM (hidden Markov model) are calculated iteratively using "Forward-Backward algorithm." The adaptation method we propose in this paper uses the intermediate results of the last iteration. The amount of storage to keep intermediate results is very small (typically 1/400) compared with that of the entire parameters. The confidence measure of the initial training and adaptive training can be reflected to the coefficients in calculating new parameters. Experiments were done on A. the same speaker several months between training and adaptive training/decoding B. different speakers In the case of the same speaker the recognition errors were reduced by 1/2 to 2/3 compared with non-adaptation case. However, for different speakers, only a slight improvement were obtained.