Integrating natural language constraints into HMM-based speech recognition

An approach to implementing spoken language systems is discussed. This approach takes full advantage of syntactic and semantic constraints provided by a natural language processing component in the speed understanding task and provides a tractable search space. The results indicate that the approach is a promising one for large-vocabulary spoken language systems. Parse times within a factor of 20 of real time are achieved for high-perplexity syntactic grammars with resulting hidden Markov model recognition computational requirements (2500 active words/frame) that are well within the capability of high-speed multiprocessor computers or special-purpose speech recognition hardware.<
>