An efficient top-down parsing algorithm for understanding speech by using stochastic syntactic and semantic models

The paper is concerning an approach for understanding speech using a new form of probabilistic models to represent syntactic and semantic knowledge of a restricted domain. One important feature of our grammar is that the parse tree directly represents the semantic content of the utterance. Since we determine that semantic content by an integrated search, we avoid consistency problems at the interface between the recognizer and the language understanding part of the speech understanding system. We succeeded in designing such an incremental algorithm, which integrates semantic, syntactic, and acoustic-phonetic knowledge in a seamless, consistent way. High efficiency is achieved by using a chart-parsing technique with structure-sharing and a strict top-down strategy for opening new word hypotheses in the pronunciation layer