  In this paper we demonstrate the effectiveness of non-causal context for
mitigating the effects of reverberation in deep-learning-based automatic speech
recognition (ASR) systems. First, the value of non-causal context using a
non-causal FIR filter is shown by comparing the contributions of previous vs.
future information. Second, MLP- and LSTM-based dereverberation networks were
trained to confirm the effects of causal and non-causal context when used in
ASR systems trained with clean speech. The non-causal deep-learning-based
dereverberation provides a 45% relative reduction in word error rate (WER)
compared to the popular weighted prediction error (WPE) method in experiments
with clean training in the REVERB challenge. Finally, an expanded
multicondition training procedure used in combination with a semi-enhanced test
utterance generation based on combinations of reverberated and dereverberated
signals is proposed to reduce any artifacts or distortion that may be
introduced by the non-causal dereverberation methods. The combination of both
approaches provided average relative reductions in WER equal to 10.9% and 6.0%
when compared to the baseline system obtained with the most recent REVERB
challenge recipe without and with WPE, respectively.
