  Visual speech recognition (VSR) aims to recognize the content of speech based
on lip movements, without relying on the audio stream. Advances in deep
learning and the availability of large audio-visual datasets have led to the
development of much more accurate and robust VSR models than ever before.
However, these advances are usually due to the larger training sets rather than
the model design. Here we demonstrate that designing better models is equally
as important as using larger training sets. We propose the addition of
prediction-based auxiliary tasks to a VSR model, and highlight the importance
of hyperparameter optimization and appropriate data augmentations. We show that
such a model works for different languages and outperforms all previous methods
trained on publicly available datasets by a large margin. It even outperforms
models that were trained on non-publicly available datasets containing up to to
21 times more data. We show, furthermore, that using additional training data,
even in other languages or with automatically generated transcriptions, results
in further improvement.
