Bayesian feature enhancement for large vocabulary speech recognition in the presence of noise and reverberation

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The goal of this project is the development of a large vocabulary continuous speech recognition system that is robust towards noise and reverberation, which is the typical kind of distortion if the speech is captured by distant microphones. In order to guarantee a wide applicability of the developed solutions, the availability of only single-channel recordings is assumed. The starting point of the investigations is on the one hand a Bayesian feature enhancement method that has been developed by one partner and which has been shown to be very effective on small recognition tasks. On the other hand there is the large vocabulary continuous speech recognition (LVCSR) system of the other project partner, which has been used successfully in many international projects and benchmarks. The Bayesian feature enhancement algorithm will be further developed to meet the higher requirements of a large vocabulary task. Further the interaction of the feature enhancement with the sophistated LVCSR system has to be investigated for an optimal integration, in order to realize a powerful large vocabulary recognition system for distant speech.