A hybrid algorithm for speaker adaptation using MAP transformation and adaptation

We present a hybrid algorithm for adapting a set of speaker-independent hidden Markov models (HMM's) to a new speaker based on a combination of maximum a posteriori (MAP) parameter transformation and adaptation. The algorithm is developed by first transforming clusters of HMM parameters through a class of transformation functions, Then, the transformed HMM parameters are further smoothed via Bayesian adaptation, The proposed transformation/adaptation process can be iterated for any given amount of adaptation data, and it converges rapidly in terms of likelihood improvement, The algorithm also gives a better speech recognition performance than that obtained using transformation or adaptation alone for almost any practical amount of adaptation data.