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

Felix Weninger,Shinji Watanabe,Yuuki Tachioka,Björn Schuller

Acoustics, Speech and Signal Processing（2014）

引用 96|浏览110

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摘要

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 %).

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关键词

feature extraction,recurrent neural nets,reverberation,signal denoising,speech codecs,speech recognition,ASR,automatic speech recognition,blind dereverberation,blind feature space dereverberation,deep recurrent denoising auto-encoder,feature transformations,fusion scheme,hands-free human-machine interaction,model adaptation,multi-condition training,reverberated environments,reverberated speech recognition,word error rates,De-reverberation,automatic speech recognition,feature enhancement,recurrent neural networks

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