  This work presents a scalable solution to open-vocabulary visual speech
recognition. To achieve this, we constructed the largest existing visual speech
recognition dataset, consisting of pairs of text and video clips of faces
speaking (3,886 hours of video). In tandem, we designed and trained an
integrated lipreading system, consisting of a video processing pipeline that
maps raw video to stable videos of lips and sequences of phonemes, a scalable
deep neural network that maps the lip videos to sequences of phoneme
distributions, and a production-level speech decoder that outputs sequences of
words. The proposed system achieves a word error rate (WER) of 40.9% as
measured on a held-out set. In comparison, professional lipreaders achieve
either 86.4% or 92.9% WER on the same dataset when having access to additional
types of contextual information. Our approach significantly improves on other
lipreading approaches, including variants of LipNet and of Watch, Attend, and
Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

Computer Vision and Pattern Recognition;Machine Learning