COMPUTER VISION

LiRA: Learning Visual Speech Representations from Audio through Self-supervision

October 12, 2021

Abstract

The large amount of audiovisual content being shared online today has drawn substantial attention to the prospect of audio- visual 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. How- ever, 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.

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AUTHORS

Written by

Pingchuan Ma

Rodrigo Mira

Stavros Petridis

Bjorn W. Schuller

Maja Pantic

Publisher

Interspeech 2021

Research Topics

Computer Vision

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