A CONTINUOUS SPEECH RECOGNITION SYSTEM USING A MODIFIED LVQ2 METHOD AND A DEPENDENCY GRAMMAR WITH SEMANTIC CONSTRAINTS

This paper describes an overview of a continuous speech recognition system composed of an acoustic processor and a linguistic processor. The system deals with 843 conceptual words and 431 functional words. We have constructed an acoustic processor using a modified learning vector quantization method (MLVQ2) for phoneme recognition. The phoneme recognition score was 85.5% for 226 sentences uttered by two male speakers. The linguistic processor is composed of a processor for spotting bunsetsu units (i.e. units similar to a “phrase” in English) and a syntactic processor. The structure of the bunsetsu unit is effectively described by a finite-state automaton, the test-set word-perplexity of which is 230. In the processor for spotting bunsetsu units, using a syntax-driven continuous-DP matching algorithm, the bunsetsu units are spotted from a recognized phoneme sequence and then a bunsetsu unit lattice is generated. In the syntactic processor, the bunsetsu unit lattice is parsed based on the dependency grammar, which is expressed as the correspondence between a FEATURE marker in a modifier-bunsetsu and a SLOT-FILLER marker in a head-bunsetsu. The recognition scores of the bunsetsu units and conceptual words were 75.2% and 88.9% respectively for 226 sentences uttered by the two male speakers.