CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations

• URL: http://arxiv.org/abs/2210.02592v3
• Date: Sat, 13 May 2023 21:42:19 GMT
• Title: CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations
• Authors: Vasista Sai Lodagala and Sreyan Ghosh and S. Umesh
• Abstract summary: 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. 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.
• Score: 1.2031796234206138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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