Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
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Electrical Engineering and Systems Science > Audio and Speech Processing
Title:Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
Abstract:Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a speech large language model operating in chunk-wise streaming manner. We further introduce a reasoning-augmented variant that incorporates chain-of-thought reasoning over conversational context and the assigned role. We construct RolePlayConv, a large-scale synthetic dataset of spoken multi-party conversations with diverse assistant roles. Experiments on real-world meeting data and RolePlayConv show improved turn-taking precision by over 40% and recall by more than 70%, while substantially reducing false-positive interruptions compared to non-role-conditioned baselines.
| Comments: | Accepted for publication at Interspeech 2026 |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.13544 [eess.AS] |
| (or arXiv:2606.13544v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2606.13544
arXiv-issued DOI via DataCite (pending registration)
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