Improvements In Speech Understanding Accuracy Through The Integration Of Hierarchical Linguistic, Prosodic, And

This paper explores some issues in designing conversational systems with integrated higher level constraints. We experiment with a configuration that combines a context-dependent acoustic front-end, using MIT's SUMMIT recognizer, with ANGIE, a hierarchical framework that models word substructure and phonological processes, and with TINA, a trainable probabilistic natural language (NL) model. Working in the Jupiter weather domain, we develop a computationally tractable system which incorporates higher level linguistic, prosodic and phonological constraints together in the second of a two-pass strategy. Experiments are evaluated using a new understanding performance metric, and the new integrated system achieves up to 17.1% relative reduction in understanding error and 15.4% reduction in word error. In addition, we investigate the possibilities of a twopass system which relies on the first stage for pruning based on syllable-level constraint, and applies linguistic and prosodic knowledge largely at the second stage