Plug-and-Play Co-Occurring Face Attention for Robust Audio-Visual Speaker Extraction

• URL: http://arxiv.org/abs/2505.20635v1
• Date: Tue, 27 May 2025 02:21:38 GMT
• Title: Plug-and-Play Co-Occurring Face Attention for Robust Audio-Visual Speaker Extraction
• Authors: Zexu Pan, Shengkui Zhao, Tingting Wang, Kun Zhou, Yukun Ma, Chong Zhang, Bin Ma,
• Abstract summary: We introduce a plug-and-play inter-speaker attention module to process flexible numbers of co-occurring faces.<n>Our approach consistently outperforms baselines in experiments on diverse datasets.
• Score: 37.680463374382235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-visual speaker extraction isolates a target speaker's speech from a mixture speech signal conditioned on a visual cue, typically using the target speaker's face recording. However, in real-world scenarios, other co-occurring faces are often present on-screen, providing valuable speaker activity cues in the scene. In this work, we introduce a plug-and-play inter-speaker attention module to process these flexible numbers of co-occurring faces, allowing for more accurate speaker extraction in complex multi-person environments. We integrate our module into two prominent models: the AV-DPRNN and the state-of-the-art AV-TFGridNet. Extensive experiments on diverse datasets, including the highly overlapped VoxCeleb2 and sparsely overlapped MISP, demonstrate that our approach consistently outperforms baselines. Furthermore, cross-dataset evaluations on LRS2 and LRS3 confirm the robustness and generalizability of our method.

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