This paper deals with a hybrid NN/HMM architecture for continuous speech recognition. We present a novel approach to set up a neural linear or nonlinear feature transformation that is used as a preprocessor on top of the HMM system's RBF-network to produce discriminative feature vectors that are well suited for being modeled by mixtures of Gaussian distributions. In order to omit the computational cost of discriminative training of a context-dependent system, we propose to train a discriminant neural feature transformation on a system of low complexity and reuse this transformation in the context-dependent system to output improved feature vectors. The resulting hybrid system is an extension of a state-of-the-art continuous HMM system, and in fact, it is the first hybrid system that really is capable of outperforming these standard systems with respect to the recognition accuracy, without the need for discriminative training of the entire system. In experiments carried out on the Resource Management 1000-word continuous speech recognition task we achieved a relative error reduction of about 10% with a recognition system that, even before, was among the best ever observed on this task.
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