Estimating pronunciation variations from acoustic likelihood score for HMM reconstruction

It is widely acknowledged that pronunciation modeling is an efficient way to improve recognition performance in spontaneous speech. In pronunciation modeling, almost all methods of generating variation probability are based on relative frequency counting from DP alignment. In this paper, we investigate the local model mismatching caused by pronunciation variations and propose to estimate variation probability from acoustic likelihood score. According to estimated probability, we present a method of reconstructing pre-trained HMM models to include alternate pronunciations by sharing optimal mixture components instead of distributions. Experimental results show that using reconstructed HMM set reduces syllable error rate by 2.03% absolutely compared to the baseline system, also the accuracy improvement gained from proposed method is almost double with respect to that from previous DP alignment.