  Self-supervised learning representation (SSLR) has demonstrated its
significant effectiveness in automatic speech recognition (ASR), mainly with
clean speech. Recent work pointed out the strength of integrating SSLR with
single-channel speech enhancement for ASR in noisy environments. This paper
further advances this integration by dealing with multi-channel input. We
propose a novel end-to-end architecture by integrating dereverberation,
beamforming, SSLR, and ASR within a single neural network. Our system achieves
the best performance reported in the literature on the CHiME-4 6-channel track
with a word error rate (WER) of 1.77%. While the WavLM-based strong SSLR
demonstrates promising results by itself, the end-to-end integration with the
weighted power minimization distortionless response beamformer, which
simultaneously performs dereverberation and denoising, improves WER
significantly. Its effectiveness is also validated on the REVERB dataset.

Sound;Audio and Speech Processing