Using Dialog-Level Knowledge Sources to Improve Speech Recognition

Alexander G. Hauptmann, Sheryl R. Young, Wayne H. Ward

We motivate and describe an implementation of the MINDS* speech recognition system. MINDS uses knowledge of dialog structures, user goals and focus in a problem solving situation. The knowledge is combined to form predictions which translate into dynamically generated semantic network grammars. An experiment evaluated recognition accuracy given different levels of knowledge as constraints. Our results show that speech recognition accuracy improves dramatically, when the maximally constrained dynamic network grammar is used to process the speech input signal.

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