  Automatic speech recognition in reverberant conditions is a challenging task
as the long-term envelopes of the reverberant speech are temporally smeared. In
this paper, we propose a neural model for enhancement of sub-band temporal
envelopes for dereverberation of speech. The temporal envelopes are derived
using the autoregressive modeling framework of frequency domain linear
prediction (FDLP). The neural enhancement model proposed in this paper performs
an envelop gain based enhancement of temporal envelopes and it consists of a
series of convolutional and recurrent neural network layers. The enhanced
sub-band envelopes are used to generate features for automatic speech
recognition (ASR). The ASR experiments are performed on the REVERB challenge
dataset as well as the CHiME-3 dataset. In these experiments, the proposed
neural enhancement approach provides significant improvements over a baseline
ASR system with beamformed audio (average relative improvements of 21% on the
development set and about 11% on the evaluation set in word error rates for
REVERB challenge dataset).
