Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition. (arXiv:2110.04934v2 [cs.CL] UPDATED)

The goal of self-supervised learning (SSL) for automatic speech recognition
(ASR) is to learn good speech representations from a large amount of unlabeled
speech for the downstream ASR task. However, most SSL frameworks do not
consider noise robustness which is crucial for real-world applications. In this
paper we propose wav2vec-Switch, a method to encode noise robustness into
contextualized representations of speech via contrastive learning.
Specifically, we feed original-noisy speech pairs simultaneously into the
wav2vec 2.0 network. In addition to the …

arxiv learning speech speech recognition