Discriminatively trained mixture models in continuous speech recognition

Abstract

A method of a continuous speech recognition system is given for discriminatively training hidden Markov for a system recognition vocabulary. An input word phrase is converted into a sequence of representative frames. A correct state sequence alignment with the sequence of representative frames is determined, the correct state sequence alignment corresponding to models of words in the input word phrase. A plurality of incorrect recognition hypotheses is determined representing words in the recognition vocabulary that do not correspond to the input word phrase, each hypothesis being a state sequence based on the word models in the acoustic model database. A correct segment of the correct word model state sequence alignment is selected for discriminative training. A frame segment of frames in the sequence of representative frames is determined that corresponds to the correct segment. An incorrect segment of a state sequence in an incorrect recognition hypothesis is selected, the incorrect segment corresponding to the frame segment. A discriminative adjustment is performed on selected states in the correct segment and the corresponding states in the incorrect segment.

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