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This paper presents the details of the SRIB-LEAP submission to theConferencingSpeech challenge 2021. The challenge involved the task ofmulti-channel speech enhancement to improve the quality of far field speechfrom microphone arrays in a video conferencing room. We propose a two stagemethod involving a beamformer followed by single channel enhancement. For thebeamformer, we incorporated self-attention mechanism as inter-channelprocessing layer in the filter-and-sum network (FaSNet), an end-to-endtime-domain beamforming system. The single channel speech enhancement is donein log spectral domain using convolution neural network (CNN)-long short termmemory (LSTM) based architecture. We achieved improvements in objective qualitymetrics - perceptual evaluation of speech quality (PESQ) of 0.5 on the noisydata. On subjective quality evaluation, the proposed approach improved the meanopinion score (MOS) by an absolute measure of 0.9 over the noisy audio.

SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing