2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | MLSP-11.2 | ||
| Paper Title | Self-training and Pre-training are complementary for Speech Recognition | ||
| Authors | Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko, Paden Tomasello, Alexis Conneau, Ronan Collobert, Gabriel Synnaeve, Michael Auli, Facebook AI Research, United States | ||
| Session | MLSP-11: Self-supervised Learning for Speech Processing | ||
| Location | Gather.Town | ||
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 | ||
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 | ||
| Presentation | Poster | ||
| Topic | Machine Learning for Signal Processing: [MLR-SSUP] Self-supervised and semi-supervised learning | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | 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 resource-setups. Using just 10 minutes of labeled data from Libri-light as well as 53k hours of unlabeled data from LibriVox achieves word error rates (WER) of 2.8%/4.8% 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 WERs of 1.5%/3.1%. | ||