发表机构
Korea Advanced Institute of Science and Technology（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全双工对话模型单侧评估的不足，提出DyaFDB二元评估框架，让两个模型直接对话并双向评分，通过140场景、7560对话验证了模型间相互塑造，强调互为考官与被考者。
AI 中文摘要
全双工语音对话模型能够同时进行听和说，使语音代理能够实现基于轮次系统无法提供的自然、低延迟交互。然而，它们通常针对单侧对话者进行评估：预先录制的音频无法做出反应，或自动考官虽能实时反应但仅执行固定序列的测试且从不被评分。这些单侧框架仅评估了双体问题的一半，其中轮流说话、重叠和打断是两个耦合说话者的共同产物。我们提出DyaFDB，一个在二元设置中评估全双工模型的框架：两个模型在分配的角色下直接对话，目标可以是合作或冲突的，双方均通过外部评判离线评分。DyaFDB探究两个模型如何相互表现，例如它们如何在不同的兴趣下轮流发言或承担分配的角色。我们将四个任务实例化为140个场景，并记录了7,560段对话，覆盖六种自我和交叉配对。在整个实验中，我们观察到模型的行为持续重塑其伙伴。因此，我们证明每个模型必须同时是对方的考官和被考者，且没有任何固定的单一对话者能同时扮演这两个角色。我们将发布两个全双工模型之间的场景、角色提示和记录协议，不包含任何预先录制的音频。
英文摘要
Full-duplex spoken dialogue models listen and speak at the same time, enabling voice agents to have natural, low-latency interactions that turn-based systems cannot offer. However, they are commonly evaluated against single-sided interlocutors: pre-recorded audio that cannot react, or an automated examiner that reacts in real time but only administers a fixed sequence of tests and is never graded. These single-sided frameworks evaluate only half of a two-body problem, where turn-taking, overlap, and interruption are joint products of two coupled speakers. We propose DyaFDB, a framework that evaluates full-duplex models in a dyadic setup: two models converse directly under assigned roles with cooperative or conflicting goals, and both sides are scored offline with an external judge. DyaFDB probes how the two models behave toward each other, such as how they take turns or carry an assigned role under different interests. We instantiate four tasks as 140 scenarios and record 7,560 conversations, covering six self- and cross-play pairings. Throughout the experiments, we observe that how a model behaves continually reshapes its partner. We thus demonstrate that each model must be both the examiner and examinee of the other, and no single fixed interlocutor can play both parts. We will release the scenarios, role prompts, and recording protocols between two full-duplex models, without any pre-recorded audio.
CommentsProject page: https://dyafdb.github.io/
