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ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations

We propose a novel strategy ES3 for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video we reframe the problem as the acquisition of shared unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation we propose a novel "evolving" strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next we incorporate video-unique speech information and bootstrap the audio-visual representations on top of the previously acquired shared knowledge. Finally we maximize the total audio-visual speech information including synergistic information to obtain robust and comprehensive representations. We implement ES3 as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular on LRS2-BBC our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

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