Visual Speech Recognition (VSR) is the task of predicting spoken words from
silent lip movements. VSR is regarded as a challenging task because of the
insufficient information on lip movements. In this paper, we propose an Audio
Knowledge empowered Visual Speech Recognition framework (AKVSR) to complement
the insufficient speech information of visual modality by using audio modality.
Different from the previous methods, the proposed AKVSR 1) utilizes rich audio
knowledge encoded by a large-scale pretrained audio model, 2) saves the
linguistic information of audio knowledge in compact audio memory by discarding
the non-linguistic information from the audio through quantization, and 3)
includes Audio Bridging Module which can find the best-matched audio features
from the compact audio memory, which makes our training possible without audio
inputs, once after the compact audio memory is composed. We validate the
effectiveness of the proposed method through extensive experiments, and achieve
new state-of-the-art performances on the widely-used datasets, LRS2 and LRS3.

提出了一种基于音频知识的视觉语音识别框架（AKVSR），通过使用音频模态来补充视觉模态中不足的语音信息，利用预训练的大规模音频模型编码丰富的音频知识，并通过量化舍弃非语言信息从而将语言信息保存在紧凑的音频存储器中，并包括能够从紧凑的音频存储器中找到最佳匹配音频特征的音频桥接模块，使得训练过程不需要音频输入，通过广泛的实验验证了该方法的有效性，并在广泛使用的数据集 LRS2 和 LRS3 上取得了最新的最佳表现。