  The task of speech recognition in far-field environments is adversely
affected by the reverberant artifacts that elicit as the temporal smearing of
the sub-band envelopes. In this paper, we develop a neural model for speech
dereverberation using the long-term sub-band envelopes of speech. The sub-band
envelopes are derived using frequency domain linear prediction (FDLP) which
performs an autoregressive estimation of the Hilbert envelopes. The neural
dereverberation model estimates the envelope gain which when applied to
reverberant signals suppresses the late reflection components in the far-field
signal. The dereverberated envelopes are used for feature extraction in speech
recognition. Further, the sequence of steps involved in envelope
dereverberation, feature extraction and acoustic modeling for ASR can be
implemented as a single neural processing pipeline which allows the joint
learning of the dereverberation network and the acoustic model. Several
experiments are performed on the REVERB challenge dataset, CHiME-3 dataset and
VOiCES dataset. In these experiments, the joint learning of envelope
dereverberation and acoustic model yields significant performance improvements
over the baseline ASR system based on log-mel spectrogram as well as other past
approaches for dereverberation (average relative improvements of 10-24% over
the baseline system). A detailed analysis on the choice of hyper-parameters and
the cost function involved in envelope dereverberation is also provided.
