  Visual speech recognition models extract visual features in a hierarchical
manner. At the lower level, there is a visual front-end with a limited temporal
receptive field that processes the raw pixels depicting the lips or faces. At
the higher level, there is an encoder that attends to the embeddings produced
by the front-end over a large temporal receptive field. Previous work has
focused on improving the visual front-end of the model to extract more useful
features for speech recognition. Surprisingly, our work shows that complex
visual front-ends are not necessary. Instead of allocating resources to a
sophisticated visual front-end, we find that a linear visual front-end paired
with a larger Conformer encoder results in lower latency, more efficient memory
usage, and improved WER performance. We achieve a new state-of-the-art of 12.8%
WER for visual speech recognition on the TED LRS3 dataset, which rivals the
performance of audio-only models from just four years ago.
