CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization

• URL: http://arxiv.org/abs/2505.03186v2
• Date: Thu, 15 May 2025 07:21:04 GMT
• Title: CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization
• Authors: Detao Bai, Zhiheng Ma, Xihan Wei, Liefeng Bo,
• Abstract summary: CoGenAV is a powerful and data-efficient model designed to learn versatile audio-visual representations.<n>CoGenAV is trained by optimizing a dual objective derived from natural audio-visual synchrony, contrastive feature alignment and generative text prediction.<n>We showcase the effectiveness and versatility of the learned CoGenAV representations on multiple benchmarks.
• Score: 16.372875825530787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inherent synchronization between a speaker's lip movements, voice, and the underlying linguistic content offers a rich source of information for improving speech processing tasks, especially in challenging conditions where traditional audio-only systems falter. We introduce CoGenAV, a powerful and data-efficient model designed to learn versatile audio-visual representations applicable across a wide range of speech and audio-visual tasks. CoGenAV is trained by optimizing a dual objective derived from natural audio-visual synchrony, contrastive feature alignment and generative text prediction, using only 223 hours of labeled data from the LRS2 dataset. This contrastive-generative synchronization strategy effectively captures fundamental cross-modal correlations. We showcase the effectiveness and versatility of the learned CoGenAV representations on multiple benchmarks. When utilized for Audio-Visual Speech Recognition (AVSR) on LRS2, these representations contribute to achieving a state-of-the-art Word Error Rate (WER) of 1.27. They also enable strong performance in Visual Speech Recognition (VSR) with a WER of 20.5 on LRS2, and significantly improve performance in noisy environments by over 70%. Furthermore, CoGenAV representations benefit speech reconstruction tasks, boosting performance in Speech Enhancement and Separation, and achieve competitive results in audio-visual synchronization tasks like Active Speaker Detection (ASD). Our model will be open-sourced to facilitate further development and collaboration within both academia and industry.

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