  Audio-visual automatic speech recognition (AV-ASR) introduces the video
modality into the speech recognition process, often by relying on information
conveyed by the motion of the speaker's mouth. The use of the video signal
requires extracting visual features, which are then combined with the acoustic
features to build an AV-ASR system [1]. This is traditionally done with some
form of 3D convolutional network (e.g. VGG) as widely used in the computer
vision community. Recently, image transformers [2] have been introduced to
extract visual features useful for image classification tasks. In this work, we
propose to replace the 3D convolutional visual front-end with a video
transformer front-end. We train our systems on a large-scale dataset composed
of YouTube videos and evaluate performance on the publicly available LRS3-TED
set, as well as on a large set of YouTube videos. On a lip-reading task, the
transformer-based front-end shows superior performance compared to a strong
convolutional baseline. On an AV-ASR task, the transformer front-end performs
as well as (or better than) the convolutional baseline. Fine-tuning our model
on the LRS3-TED training set matches previous state of the art. Thus, we
experimentally show the viability of the convolution-free model for AV-ASR.
