  Self-training and unsupervised pre-training have emerged as effective
approaches to improve speech recognition systems using unlabeled data. However,
it is not clear whether they learn similar patterns or if they can be
effectively combined. In this paper, we show that pseudo-labeling and
pre-training with wav2vec 2.0 are complementary in a variety of labeled data
setups. Using just 10 minutes of labeled data from Libri-light as well as 53k
hours of unlabeled data from LibriVox achieves WERs of 3.0%/5.2% on the clean
and other test sets of Librispeech - rivaling the best published systems
trained on 960 hours of labeled data only a year ago. Training on all labeled
data of Librispeech achieves WERs of 1.5%/3.1%.
