Knowledge Integration for Improving Performance in LVCSR

Abstract

This paper presents a knowledge integration framework to improve performance in large vocabulary continuous speech recognition. Two types of knowledge sources, manner attribute and prosodic structure, are incorporated. For manner of articulation, six attribute detectors trained with an American English corpus (WSJ0) are utilized to rescore hypothesized phones in word lattices obtained by a baseline ASR system. For the prosodic structure, models trained with an unsupervised joint prosody labeling and modeling (PLM) technique using WSJ0 are used in lattice rescoring. Experimental results on the American English WSJ word recognition task of the Nov92 test set show that the proposed approach significantly outperforms the baseline system that does not use articulatory and prosodic information. The results also demonstrate the effectiveness and usefulness of the PLM technique in constructing prosodic models for American English ASR.