  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 existing contrastive learning task, we
switch the quantized representations of the original and noisy speech as
additional prediction targets of each other. By doing this, it enforces the
network to have consistent predictions for the original and noisy speech, thus
allows to learn contextualized representation with noise robustness. Our
experiments on synthesized and real noisy data show the effectiveness of our
method: it achieves 2.9--4.9% relative word error rate (WER) reduction on the
synthesized noisy LibriSpeech data without deterioration on the original data,
and 5.7% on CHiME-4 real 1-channel noisy data compared to a data augmentation
baseline even with a strong language model for decoding. Our results on CHiME-4
can match or even surpass those with well-designed speech enhancement
components.
