The goal of this paper is to learn strong lip reading models that can
recognise speech in silent videos. Most prior works deal with the open-set
visual speech recognition problem by adapting existing automatic speech
recognition techniques on top of trivially pooled visual features. Instead, in
this paper we focus on the unique challenges encountered in lip reading and
propose tailored solutions. To this end, we make the following contributions:
(1) we propose an attention-based pooling mechanism to aggregate visual speech
representations; (2) we use sub-word units for lip reading for the first time
and show that this allows us to better model the ambiguities of the task; (3)
we propose a model for Visual Speech Detection (VSD), trained on top of the lip
reading network. Following the above, we obtain state-of-the-art results on the
challenging LRS2 and LRS3 benchmarks when training on public datasets, and even
surpass models trained on large-scale industrial datasets by using an order of
magnitude less data. Our best model achieves 22.6% word error rate on the LRS2
dataset, a performance unprecedented for lip reading models, significantly
reducing the performance gap between lip reading and automatic speech
recognition. Moreover, on the AVA-ActiveSpeaker benchmark, our VSD model
surpasses all visual-only baselines and even outperforms several recent
audio-visual methods.

该研究提出了一种关注视觉嘴唇读取的独特挑战，采用定制的基于注意力和亚词单元的方法，构建视觉嘴唇读取模型和视觉语音检测模型，并在公共数据集上实现了当今最优秀的结果，甚至超过工业声音数据集训练模型大约一个数量级的数据。