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

AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model