MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild
arXiv:2606.16731v1 Announce Type: cross 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 interac
The continuous evolution of AI in human-robot interaction necessitates more robust and adaptable perception models, moving beyond constrained lab environments to real-world complexity.
This development improves human-robot collaboration in uncontrolled environments, crucial for the broader adoption of robots in practical applications by addressing a core challenge of natural interaction.
Turn-taking prediction systems can now operate effectively with simpler, more widely available sensor setups, reducing the cost and complexity of deploying interactive AI/robotics.
- · Human-robot interaction developers
- · AI agents developers
- · Robotics companies
- · Smart device manufacturers
- · Developers reliant on complex, multi-sensor setups
- · Companies manufacturing expensive, specialized microphone arrays
Improved, more seamless human-robot communication will accelerate the development of autonomous systems in diverse fields.
The reduced hardware requirements could democratize access to advanced interactive AI for a wider range of applications and industries.
More natural human-robot interaction could lead to increased societal acceptance and integration of AI agents and robots into daily life.
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Read at arXiv cs.AI