n this paper we report our recent research whose goal is 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 efficiently

computes the likelihood of any observation utterance while optimizing the

dynamic regimes in the speech model. The effectiveness of the algorithm is tested

on the speech data in the Switchboard corpus, in which the optimized dynamic

regimes computed from the algorithm are compared with those from exhaustive

search. We also present speech recognition results on the Switchboard corpus that

demonstrate improvements of the recognizer’s performance compared with the

use of the dynamic regimes heuristically set from the phone segmentation by a

state-of-the-art hidden Markov model (HMM) system.

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In  Computer, Speech and Language. Academic Press

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