  The goal of this work is to train strong models for visual speech recognition
without requiring human annotated ground truth data. We achieve this by
distilling from an Automatic Speech Recognition (ASR) model that has been
trained on a large-scale audio-only corpus. We use a cross-modal distillation
method that combines Connectionist Temporal Classification (CTC) with a
frame-wise cross-entropy loss. Our contributions are fourfold: (i) we show that
ground truth transcriptions are not necessary to train a lip reading system;
(ii) we show how arbitrary amounts of unlabelled video data can be leveraged to
improve performance; (iii) we demonstrate that distillation significantly
speeds up training; and, (iv) we obtain state-of-the-art results on the
challenging LRS2 and LRS3 datasets for training only on publicly available
data.

Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing