arXiv:2606.13544v1 Announce Type: cross 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
Advances in large language models and real-time speech processing have reached a point where sophisticated, adaptive turn-taking in multi-party conversations is becoming technically feasible.
This development addresses a critical bottleneck in human-AI interaction, enabling more natural and effective multi-party voice agents that can seamlessly integrate into complex conversational dynamics.
AI voice agents can now move beyond simple command-response functions to actively participate and mediate complex group discussions, conditioning their behavior on explicitly assigned roles and conversational context.
- · AI developers
- · Customer service industries
- · Conference call platforms
- · Collaborative software
- · Monolithic voice assistant platforms (without adaptive capabilities)
- · Inefficient meeting structures
- · Developers of rule-based turn-taking systems
More fluid and efficient human-AI collaboration in real-time spoken interactions will emerge.
The demand for high-quality, real-time speech processing and conversational AI will accelerate across various enterprise applications.
AI agents may begin to take on more executive and managerial roles in meetings, mediating discussions and managing information flow.
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