Instruct-FD：你的全双工语音系统能遵循轮流发言指令吗？
Instruct-FD: Can Your Full-Duplex Speech System Follow Turn-Taking Instructions?
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中文总结 AI 辅助
研究全双工语音系统在明确指令下的轮流发言行为，引入Instruct-FD基准，开发相关工具，通过对六个先进系统测试发现遵循指令的轮流管理有差距，为构建适应性全双工对话系统指明关键方向。
中文摘要 AI 辅助
当前的全双工口语对话系统能产生流畅的交互，但尚不清楚它们在收到明确指令时能否调整轮流发言行为，这对实际部署至关重要。我们引入Instruct-FD，一个用于评估全双工系统中可控轮流管理的指令条件基准。为此开发了人工验证、可扩展的合成管道等。对六个先进全双工系统的基准测试发现，在遵循指令的轮流管理方面存在显著差距，最佳模型的遵循率仅64.4%。这些发现表明遵循指令的轮流管理是构建适应性和可部署全双工对话系统的关键方向。
英文摘要
Current full-duplex (FD) spoken dialogue systems can produce fluid interactions, yet it remains unclear whether they can adapt their turn-taking behavior when explicitly instructed. This is critical for real-world deployment, where conversational policies vary across applications (e.g., proactive tutoring vs. passive counseling). We introduce Instruct-FD, an instruction-conditioned benchmark for evaluating controllable turn management in FD systems. To enable this, we develop a human-validated, scalable synthetic pipeline that generates instruction-conditioned conversations, along with a deployment-agnostic multi-turn evaluation protocol and an LLM-based judge. Benchmarking six state-of-the-art full-duplex systems reveals a substantial gap in instruction-following turn management: the best model achieves only 64.4% adherence. Performance is highly uneven across behaviors and scenarios, with proactive behaviors such as model backchanneling and interruption remaining particularly challenging. These findings establish instruction-following turn management as a crucial direction for building adaptable and deployable full-duplex dialogue systems.
发表机构
- Boson AI（玻色子人工智能公司）
机构由 AI 辅助整理，请以论文原文为准。
