  In this paper, we exploit the effective way to leverage contextual
information to improve the speech dereverberation performance in real-world
reverberant environments. We propose a temporal-contextual attention approach
on the deep neural network (DNN) for environment-aware speech dereverberation,
which can adaptively attend to the contextual information. More specifically, a
FullBand based Temporal Attention approach (FTA) is proposed, which models the
correlations between the fullband information of the context frames. In
addition, considering the difference between the attenuation of high frequency
bands and low frequency bands (high frequency bands attenuate faster than low
frequency bands) in the room impulse response (RIR), we also propose a SubBand
based Temporal Attention approach (STA). In order to guide the network to be
more aware of the reverberant environments, we jointly optimize the
dereverberation network and the reverberation time (RT60) estimator in a
multi-task manner. Our experimental results indicate that the proposed method
outperforms our previously proposed reverberation-time-aware DNN and the
learned attention weights are fully physical consistent. We also report a
preliminary yet promising dereverberation and recognition experiment on real
test data.
