arXiv:2606.16568v1 Announce Type: new Abstract: Reliable turn-taking is essential for spoken dialogue systems. However, most existing methods are designed for two-speaker interaction and struggle with realistic multiparty audio containing overlap and rapid speaker changes. We study multiparty turn-taking on the VoxConverse dataset and propose an audio-only two-stage pipeline that separates when to trigger a turn boundary from whether the floor is actually transferring. A fast trigger scans the audio and proposes candidate end-of-turn times, while a lightweight verifier runs only at those times
The increasing sophistication of AI models and the demand for more natural human-computer interaction are driving continuous research into advanced dialogue systems, including turn-taking in multiparty scenarios.
Reliable multiparty turn-taking is crucial for developing robust, natural, and user-friendly AI assistants and robotic interfaces, enhancing their integration into complex social environments.
This research outlines a more effective architecture for managing real-time conversations with multiple participants, enabling AI systems to better interpret and participate in dynamic dialogues.
- · AI assistant developers
- · Robotics companies
- · Teleconferencing platforms
- · Developers of simple rule-based dialogue systems
Improved conversational AI leading to more natural human-AI interactions across various applications.
Reduced friction in AI-mediated multiparty communication, potentially increasing adoption of AI in meetings and collaborative work.
Enhanced AI capability in complex social settings could accelerate the development of more general-purpose AI agents interacting seamlessly with groups.
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Read at arXiv cs.CL