Turn-taking modeling is fundamental to spoken dialogue systems, yet its evaluation remains fragmented and often limited to binary boundary detection under narrow interaction settings. Such protocols hinder systematic comparison and obscure model weaknesses across conversational conditions. We present CoDeTT, a context-aware decision benchmark for turn-taking evaluation. CoDeTT formulates turn-taking as a structured decision problem and constructs a multi-scenario dataset with fine-grained decision categories and controlled context variations. Under a unified evaluation protocol, we assess representative existing models and observe substantial performance disparities across decision types and interaction scenarios. CoDeTT provides a standardized benchmark for systematic and context-aware evaluation of turn-taking systems. The benchmark dataset and evaluation toolkit are available at https://github.com/YingaoWang-casia/CoDeTT.github.io.

翻译：话轮转换建模是口语对话系统的基础，然而其评估仍较为零散，且通常局限于狭隘交互场景下的二元边界检测。此类评估协议阻碍了系统性比较，并掩盖了模型在不同对话条件下的弱点。我们提出CoDeTT，一种用于话轮转换评估的上下文感知决策基准。CoDeTT将话轮转换形式化为结构化决策问题，并构建了一个包含细粒度决策类别和受控上下文变体的多场景数据集。在统一的评估协议下，我们评估了若干代表性现有模型，并观察到模型在决策类型和交互场景上存在显著的性能差异。CoDeTT为话轮转换系统提供了用于系统化、上下文感知评估的标准化基准。该基准数据集及评估工具包可从 https://github.com/YingaoWang-casia/CoDeTT.github.io 获取。