  Speech applications in far-field real world settings often deal with signals
that are corrupted by reverberation. The task of dereverberation constitutes an
important step to improve the audible quality and to reduce the error rates in
applications like automatic speech recognition (ASR). We propose a unified
framework of speech dereverberation for improving the speech quality and the
ASR performance using the approach of envelope-carrier decomposition provided
by an autoregressive (AR) model. The AR model is applied in the frequency
domain of the sub-band speech signals to separate the envelope and carrier
parts. A novel neural architecture based on dual path long short term memory
(DPLSTM) model is proposed, which jointly enhances the sub-band envelope and
carrier components. The dereverberated envelope-carrier signals are modulated
and the sub-band signals are synthesized to reconstruct the audio signal back.
The DPLSTM model for dereverberation of envelope and carrier components also
allows the joint learning of the network weights for the down stream ASR task.
In the ASR tasks on the REVERB challenge dataset as well as on the VOiCES
dataset, we illustrate that the joint learning of speech dereverberation
network and the E2E ASR model yields significant performance improvements over
the baseline ASR system trained on log-mel spectrogram as well as other
benchmarks for dereverberation (average relative improvements of 10-24% over
the baseline system). The speech quality improvements, evaluated using
subjective listening tests, further highlight the improved quality of the
reconstructed audio.

Audio and Speech Processing;Artificial Intelligence;Sound