  This work presents a large-scale audio-visual speech recognition system based
on a recurrent neural network transducer (RNN-T) architecture. To support the
development of such a system, we built a large audio-visual (A/V) dataset of
segmented utterances extracted from YouTube public videos, leading to 31k hours
of audio-visual training content. The performance of an audio-only,
visual-only, and audio-visual system are compared on two large-vocabulary test
sets: a set of utterance segments from public YouTube videos called YTDEV18 and
the publicly available LRS3-TED set. To highlight the contribution of the
visual modality, we also evaluated the performance of our system on the YTDEV18
set artificially corrupted with background noise and overlapping speech. To the
best of our knowledge, our system significantly improves the state-of-the-art
on the LRS3-TED set.
