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Abstract:
In this paper we present a novel hybrid architecture for
continuous speech recognition systems. It consists of a continuous
HMM system extended by an arbitrary neural network that is used as
a preprocessor that takes several frames of the feature vector as
input to produce more discriminative feature vectors with respect
to the underlying HMM system. This 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.
Experimental results show a relative error reduction of about 10
achieved on a remarkably good recognition system based on
continuous HMMs for the Resource Management 1000-word continuous
speech recognition task.
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