Abstract

A discriminative training algorithm for predictive neural network models is proposed. The algorithm is applied to a speaker independent isolated digit recognition experiment. The recognition error rate is reduced from 2.52% when the classifier is trained with a non-discriminative algorithm to 0.58% when the discriminative algorithm is applied. The increase in classifier discrimination ability is also demonstrated. >

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