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openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
TL;DR
Turn-taking in multi-party conversations is challenging for voice agents due to competition and user expectations. ModeratorLM is a role-playing voice agent that improves turn-taking by conditioning responses on assigned roles.
✦ Why It Matters
Engineers can leverage role-based conditioning to enhance voice agent performance in multi-party interactions.
Key Takeaways
- Implement role-based conditioning in voice agents.
- Utilize large-scale synthetic datasets for training.
- Focus on improving turn-taking metrics in multi-party settings.
- Explore reasoning-augmented models for better context understanding.
Full Summary
Multi-party spoken conversations present significant challenges for voice-based agents, particularly in managing who speaks when, known as turn-taking. ModeratorLM is introduced as a novel voice agent that adapts its turn-taking behavior based on explicitly assigned roles within conversations.
Built on a large language model, it processes speech in a chunk-wise streaming manner and includes a reasoning-augmented variant that utilizes chain-of-thought reasoning to enhance context understanding. The researchers created RolePlayConv, a large-scale synthetic dataset of multi-party conversations featuring diverse assistant roles to train and evaluate the model.
Results from real-world meeting data and RolePlayConv demonstrate that ModeratorLM achieves over 40% improvement in turn-taking precision and more than 70% in recall, while significantly reducing false-positive interruptions compared to non-role-conditioned systems. These findings suggest that role-based conditioning can greatly enhance the effectiveness of voice agents in complex conversational environments.