  This paper presents the details of the SRIB-LEAP submission to the
ConferencingSpeech challenge 2021. The challenge involved the task of
multi-channel speech enhancement to improve the quality of far field speech
from microphone arrays in a video conferencing room. We propose a two stage
method involving a beamformer followed by single channel enhancement. For the
beamformer, we incorporated self-attention mechanism as inter-channel
processing layer in the filter-and-sum network (FaSNet), an end-to-end
time-domain beamforming system. The single channel speech enhancement is done
in log spectral domain using convolution neural network (CNN)-long short term
memory (LSTM) based architecture. We achieved improvements in objective quality
metrics - perceptual evaluation of speech quality (PESQ) of 0.5 on the noisy
data. On subjective quality evaluation, the proposed approach improved the mean
opinion score (MOS) by an absolute measure of 0.9 over the noisy audio.
