Hybrid NN/HMM-Based Speech Recognition with a Discriminant Neural Feature Extraction
Daniel Willett, Gerhard Rigoll · 1997
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 hybridsystem that really is capable of outperforming these standard systems with respect to the recognition accuracy. Experimental results show an relative error reduction of about 10% that we achieved on a remarkably good recognition system based on continuous HMMs for the Resource Management 1000-word continuous speech recognition task. 1 INTRODUCTION Standard state-of-the-art speech recognition systems utilize Hidden Markov Models (HMMs) to model the acoustic behavior of basic speech units like phones or words. Most...