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arXiv · 2606.16731

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild

Haotian Qi·Gabriel Skantze
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Abstract

Current multiparty turn-taking models often rely on complex microphone arrays or multi-camera setups, limiting their applicability in human-robot interaction scenarios. We introduce MuVAP, a causal multimodal framework that extends Voice Activity Projection by grounding acoustic predictions in face tracks, enabling speaker-aware turn-taking predictions from a monaural audio stream and a single camera view. To address the combinatorial complexity of modeling multiple speakers, we propose Role-Relative Projection, which maps any N-speaker interaction onto a fixed current versus next floor-holder state. Because existing audiovisual datasets contain disruptive editing cuts that break causal tracking, we introduce the Audio-Visual Conversation Corpus, a 31-hour dataset of unedited, single-camera multiparty conversations. Evaluations demonstrate that MuVAP outperforms strong baselines on Shift-Hold and next-speaker prediction tasks across two- and three-speaker settings.

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BibTeXRIS

Haotian Qi, Gabriel Skantze. 2026-06-15. MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild. https://arxiv.org/abs/2606.16731

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