Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
(3 minutes introduction)
| Wei-Ning Hsu (Facebook, USA), Anuroop Sriram (Facebook, USA), Alexei Baevski (Facebook, USA), Tatiana Likhomanenko (Facebook, USA), Qiantong Xu (Facebook, USA), Vineel Pratap (Facebook, USA), Jacob Kahn (Facebook, USA), Ann Lee (Facebook, USA), Ronan Collobert (Facebook, USA), Gabriel Synnaeve (Facebook, France), Michael Auli (Facebook, USA) |
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Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. With no access to in-domain labeled data, pre-training on unlabeled in-domain data closes 66–73% of the performance gap between the ideal setting of in-domain labeled data and a competitive supervised out-of-domain model. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. We will release pre-trained models.
