Modeling Consistency in a Speaker Independent Continuous Speech Recognition System

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Yochai Konig, Nelson Morgan, Chuck Wooters, Victor Abrash, Michael Cohen, Horacio Franco


We would like to incorporate speaker-dependent consistencies, such as gender, in an otherwise speaker-independent speech recognition system. In this paper we discuss a Gender Dependent Neural Network (GDNN) which can be tuned for each gender, while sharing most of the speaker independent parameters. We use a classification network to help generate gender-dependent phonetic probabilities for a statistical (HMM) recogni(cid:173) tion system. The gender classification net predicts the gender with high accuracy, 98.3% on a Resource Management test set. However, the in(cid:173) tegration of the GDNN into our hybrid HMM-neural network recognizer provided an improvement in the recognition score that is not statistically significant on a Resource Management test set.