Heuristic search integrating syntactic, semantic and dialog-level constraints

We present a new model of speech understanding, based on the cooperation of the speech recognizer and language analyzer, which interacts with the knowledge sources while keeping its modularity. The semantic analyzer is realized with a semantic network that represents the possible concepts in a task. The speech recognizer based on an LR parser interacts with the semantic analyzer to eliminate invalid hypotheses at an early stage. The coupling of a loose grammar and interactive semantic analysis accepts ill-formed sentences while filtering out non-sense ones, thus realizes robust understanding. Dialog-level knowledge is also incorporated to constrain both the syntactic and the semantic knowledge sources. The key to guide the search efficiently is powerful heuristics. The relationship between the heuristic power and search efficiency is examined experimentally. The stochastic word bigram is derived from the probabilistic LR grammar as A*-admissible heuristics