Abstract

A statistical approach to speech recognition is outlined which draws close parallel with closed-loop human speech communication schematized as a joint process of encoding and decoding of linguistic messages. The encoder consists of the symbolically-valued overlapping articulatory feature model and of its interface to a nonlinear task-dynamic model of speech production. A general speech recognizer architecture based on optimal decoding strategy incorporating encoder-decoder interactions is described and discussed.

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