Speaker adaptation based on MAP estimation of HMM parameters

A number of issues related to the application of Bayesian learning techniques to speaker adaptation are investigated. It is shown that the seed models required to construct prior densities to obtain the MAP (maximum a posteriori) estimate can be a speaker-independent (SI) model, a set of female and male models, or even a task-independent acoustic model. Speaker-adaptive training algorithms are shown to be effective in improving the performance of both speaker-dependent and speaker-independent speech recognition systems. The segmental MAP estimation formulation is used to perform adaptive acoustic modeling for speaker adaptation applications. Tested on an RM (resource management) task, it was found that supervised speaker adaptation based on two gender-dependent models gave a better result than that obtained with a single SI seed. Compared with speaker-dependent training, speaker adaptation achieved an equal or better performance with the same amount of training/adaptation data.<>