A concept graph based confidence measure

Abstract

In this paper, the confidence measure of a hypothesized word is derived from its posterior probability. In contrast to common approaches, in which N-best lists or word graphs/lattices are used, the posterior probabilities are derived from a concept graph. The concept graph is obtained from a word graph through a partial parsing process using semantic grammars. This approach allows us to use relatively complex and better language models along with acoustic models to compute word posterior probabilities. The language model used is comprised of stochastic context free grammars (one for each concept) and an n-gram concept language model. We show that the posterior probabilities computed on concept graphs outperform those computed on word graphs when used as confidence measures. Results are presented within the context of Colorado University (CU) Communicator System; a telephone-based dialog system for making travel plans by accessing information about flights, hotels and car rentals.