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Abstract

A speech recognition system has vocabulary word models having for each word model state both a discrete probability distribution function and a continuous probability distribution function. Word models are initially aligned with an input utterance using the discrete probability distribution functions, and an initial matching performed. From well scoring word models, a ranked scoring of those models is generated using the respective continuous probability distribution functions. After each utterance, preselected continuous probability distribution function parameters are discriminatively adjusted to increase the difference in scoring between the best scoring and the next ranking models. In the event a user subsequently corrects a prior recognition event by selecting a different word model from that generated by the recognition system, a re-adjustment of the continuous probability distribution function parameters is performed by adjusting the current state of the parameters opposite to the adjustment performed with the original recognition event, and adjusting the current parameters to that which would have been performed if the user correction associated word had been the best scoring model.

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