Training for polynomial segment model using the expectation maximization algorithm

One of the difficulties in using the polynomial segment model (PSM) to capture the temporal correlations within a phonetic segment is the lack of an efficient training algorithm comparable with the Baum-Welch algorithm in HMM. In our previous paper, we introduced a recursive likelihood computation algorithm for PSM recognition which can perform Viterbi-style training. In this paper, we extend the recursive likelihood computation into a fast forward-backward PSM training algorithm that maximizes PSM likelihood. In addition, we introduce an improved PSM, dynamic multi-region PSM, that allows a data-driven alignment between observations and the segment trajectory. The dynamic multi-region PSM model outperforms HMM and traditional PSM in both phone classification and phone recognition tasks on the TIMIT corpus.