  The large amount of audiovisual content being shared online today has drawn
substantial attention to the prospect of audiovisual self-supervised learning.
Recent works have focused on each of these modalities separately, while others
have attempted to model both simultaneously in a cross-modal fashion. However,
comparatively little attention has been given to leveraging one modality as a
training objective to learn from the other. In this work, we propose Learning
visual speech Representations from Audio via self-supervision (LiRA).
Specifically, we train a ResNet+Conformer model to predict acoustic features
from unlabelled visual speech. We find that this pre-trained model can be
leveraged towards word-level and sentence-level lip-reading through feature
extraction and fine-tuning experiments. We show that our approach significantly
outperforms other self-supervised methods on the Lip Reading in the Wild (LRW)
dataset and achieves state-of-the-art performance on Lip Reading Sentences 2
(LRS2) using only a fraction of the total labelled data.
