Optimization of dynamic regimes in a statistical hidden dynamic model for conversational speech recognition

This paper reports our on-going work aimimg to improve the performance of a novel speech recognizer based on an underlying statistical hidden dynamic model of phonetic reduction in the production of conversational speech. We have developed a path-stack search algorithm which e ciently computes the likelihood of any observation utterance while optimizing the dynamic regimes in the speech model. The e ectiveness of the algorithm is tested in simulation experiments It is also tested on Switchboard data where the optimized dynamic regimes by the search algorithm are compared with those from exhaustive search. Finally, we show speech recognition results on Switchboard data that demonstrate improvements of the recognizer's performance compared with use of the dynamic regimes heuristically set from the phone segmentation by a stateof-the-art HMM system.