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arXiv · 2210.10742

End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation

Yoshiki Masuyama·Xuankai Chang·Samuele Cornell·Shinji Watanabe·Nobutaka Ono
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Abstract

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.

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Yoshiki Masuyama, Xuankai Chang, Samuele Cornell, Shinji Watanabe, Nobutaka Ono. 2022-10-19. End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation. https://arxiv.org/abs/2210.10742

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