Deep recurrent de-noising auto-encoder and blind de-reverberation for reverberated speech recognition

This paper describes our joint efforts to provide robust automatic speech recognition (ASR) for reverberated environments, such as in hands-free human-machine interaction. We investigate blind feature space de-reverberation and deep recurrent de-noising auto-encoders (DAE) in an early fusion scheme. Results on the 2014 REVERB Challenge development set indicate that the DAE front-end provides complementary performance gains to multi-condition training, feature transformations, and model adaptation. The proposed ASR system achieves word error rates of 17.62 % and 36.6 % on simulated and real data, which is a significant improvement over the Challenge baseline (25.16 and 47.2 %).