COMPUTATION OF PROBABILITIES FOR AN ISLAND-DRIVEN PARSER

Island-driven parsers have interesting potential applications in automatic speech understanding (ASU). Most of the recently developed ASU systems are based on an acoustic processor (AP) and a language processor (LP). AP computes the a priori probability of the acoustic data given a linguistic interpretation. LP computes the probability of the linguistic interpretation. This paper describes an effort to adapt island-driven parsers to handle stochastic context-free grammars. These grammars could be used as language models (LM's) by a LP to compute the probability of a linguistic interpretation. Island-driven parsers applied to ASU are based on the idea of growing islands of recognized words with an attempt to recognize new words at the edges of an island. As different island may compete for growth, it is important to compute the probability that LM generates a sentence containing islands and gaps between them. This probability can be used to score competing interpretation hypotheses. Algorithms for computing these probabilities are introduced in this paper. The complexity of these algorithms is analyzed both from theoretical and practical points of view. It is shown that the computation of probabilities in the presence of gaps of unknown length requires the impractical solution of a nonlinear system of equations, whereas the computation of probabilities for cases with gaps containing a known number of unknown words has polynomial time complexity and is practically feasible. The use in ASU systems of the results obtained is discussed.