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arXiv · 2310.19644

Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction

Zexu Pan·Gordon Wichern·Yoshiki Masuyama·Francois G. Germain·Sameer Khurana·Chiori Hori·Jonathan Le Roux
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Abstract

Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers. Building upon the achievements of the state-of-the-art (SOTA) time-frequency speaker separation model TF-GridNet, we propose AV-GridNet, a visual-grounded variant that incorporates the face recording of a target speaker as a conditioning factor during the extraction process. Recognizing the inherent dissimilarities between speech and noise signals as interfering sources, we also propose SAV-GridNet, a scenario-aware model that identifies the type of interfering scenario first and then applies a dedicated expert model trained specifically for that scenario. Our proposed model achieves SOTA results on the second COG-MHEAR Audio-Visual Speech Enhancement Challenge, outperforming other models by a significant margin, objectively and in a listening test. We also perform an extensive analysis of the results under the two scenarios.

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Zexu Pan, Gordon Wichern, Yoshiki Masuyama, Francois G. Germain, Sameer Khurana, Chiori Hori, Jonathan Le Roux. 2023-10-30. Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction. https://arxiv.org/abs/2310.19644

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