| Paper: | SP-P12.12 | ||
| Session: | Acoustic Modeling: Model Complexity, General Topics | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Acoustic Modeling for Speech Recognition | ||
| Title: | TRAINING FOR POLYNOMIAL SEGMENT MODEL USING THE EXPECTATION MAXIMIZATION ALGORITHM | ||
| Authors: | Chak-Fai Li; Hong Kong University of Science and Technology | ||
| Man-Hung Siu; Hong Kong University of Science and Technology | |||
| Abstract: | One of the difficulties in using 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 and can perform Viterbi-style training. In this paper, we extend the recurrsive likelihood computation into a fast forward-backward PSM training algorithm that maximizes PSM likelihood. In addition, we introduce an improved PSM, dynamic multi-segment PSM, that allows a data-driven alignment betweens observations and the segment trajectory. The dynamic multi-segment PSM model outperforms HMM and traditional PSM in both phone classification and phone recognition tasks on the TIMIT corpus. | ||
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