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While Self-Supervised Learning has helped reap the benefit of the scale from
the available unlabeled data, the learning paradigms are continuously being
bettered. We present a new pre-training strategy named ccc-wav2vec 2.0, which
uses clustering and an augmentation-based cross-contrastive loss as its
self-supervised objective. Through the clustering module, we scale down the
influence of those negative examples that are highly similar to the positive.
The Cross-Contrastive loss is computed between the encoder output of the
original sample and the quantizer output of its augmentation and vice-versa,
bringing robustness to the pre-training strategy. ccc-wav2vec 2.0 achieves up
to 15.6% and 12.7% relative WER improvement over the baseline wav2vec 2.0 on
the test-clean and test-other sets, respectively, of LibriSpeech, without the
use of any language model. The proposed method also achieves up to 14.9%
relative WER improvement over the baseline wav2vec 2.0 when fine-tuned on
Switchboard data. We make all our codes publicly available on GitHub.
