Abstract
In this paper, we propose a new Self-Supervised Learning (SSL) algorithm called data2vec-aqc, for speech representation learning from unlabeled speech data. Our goal is to improve SSL for speech in domains where both unlabeled and labeled data are limited. Building on the recently introduced data2vec, we introduce additional modules to the data2vec framework that leverage the benefit of data augmentations, quantized representations, and clustering. The interaction between these modules helps solve the cross-contrastive loss as an additional self-supervised objective. data2vec-aqc achieves up to 14.1% and 20.9% relative WER improvement over the existing state-of-the-art data2vec system on the test-clean and test-other sets, respectively, of LibriSpeech, without the use of any language model. Our proposed model also achieves up to 17.8% relative WER improvement over the baseline data2vec when fine-tuned on Switchboard data.
Abstract (translated)
URL
https://arxiv.org/abs/2211.01246
