Chart parsing of stochastic spoken language models

Abstract

Performance in speech recognition systems has progressed to the point where it is now realistic to begin integrating speech with natural language systems to produce spoken language systems. Two factors have contributed to the advances in speech: statistical modeling of the input signal and language constraints. To produce spoken language systems, then, the grammar formalisms used in natural language systems must incorporate statistical information and efficient parsers for these stochastic language models must be developed. In this paper we outline how chart parsing techniques provide advantages in both computation and accuracy for spoken language systems. We describe a system that models all levels of the spoken language system using stochastic language models and present experimental results.