  This paper introduces a new method for multi-channel time domain speech
separation in reverberant environments. A fully-convolutional neural network
structure has been used to directly separate speech from multiple microphone
recordings, with no need of conventional spatial feature extraction. To reduce
the influence of reverberation on spatial feature extraction, a dereverberation
pre-processing method has been applied to further improve the separation
performance. A spatialized version of wsj0-2mix dataset has been simulated to
evaluate the proposed system. Both source separation and speech recognition
performance of the separated signals have been evaluated objectively.
Experiments show that the proposed fully-convolutional network improves the
source separation metric and the word error rate (WER) by more than 13% and 50%
relative, respectively, over a reference system with conventional features.
Applying dereverberation as pre-processing to the proposed system can further
reduce the WER by 29% relative using an acoustic model trained on clean and
reverberated data.
