Using MAP estimated parameters to improve HMM speech recognition performance

Hidden Markov models (HMMs) have been quite successfully applied to speech recognition tasks, but many unsolved problems still remain. HMMs do not directly model all phenomena that might be useful for recognition. This is the case, for example, for duration modeling. Mechanisms are needed to incorporate additional information into an HMM system. This paper presents a maximum a posteriori (MAP) parameter estimation approach for improving the state-duration modeling capability and incorporating a priori knowledge about the word-duration distribution into an HMM. The MAP-based approach is evaluated on a talker-independent, connected alphadigit task for various prior distributions on duration. The results-in terms of both computational complexity and recognition performance-are compared with the results of HMM-based systems trained with the traditional maximum-likelihood criterion