EMO-Reasoning：口语对话系统中情绪推理能力的基准测试
EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems
AI总结：
针对口语对话系统缺乏情绪推理评估整体系统的问题，本文提出EMO-Reasoning基准与跨轮次情绪推理评分，通过文本转语音生成数据集并评估七个系统，有效检测情绪不一致，推动情绪感知对话建模发展。
摘要：
语音情绪在人机交互中起着关键作用，影响着交互的参与度和情境感知的沟通效果。尽管口语对话系统近年来取得了进展，但仍缺乏一个评估情绪推理的整体系统。为此，我们引入了EMO-Reasoning基准，用于评估对话系统中的情绪连贯性。它利用通过文本转语音生成的精选数据集来模拟多样的情绪状态，克服了情绪语音数据稀缺的问题。我们进一步提出了跨轮次情绪推理评分，以评估多轮对话中的情绪转换。通过连续型、类别型和感知型指标对七个对话系统进行评估，我们证明该框架能有效检测情绪不一致，为改进现有对话系统提供见解。通过发布系统性的评估基准，我们旨在推动情绪感知口语对话建模向更自然、更具适应性的交互发展。
英文摘要：
Speech emotions play a crucial role in human-computer interaction, shaping engagement and context-aware communication. Despite recent advances in spoken dialogue systems, a holistic system for evaluating emotional reasoning is still lacking. To address this, we introduce EMO-Reasoning, a benchmark for assessing emotional coherence in dialogue systems. It leverages a curated dataset generated via text-to-speech to simulate diverse emotional states, overcoming the scarcity of emotional speech data. We further propose the Cross-turn Emotion Reasoning Score to assess the emotion transitions in multi-turn dialogues. Evaluating seven dialogue systems through continuous, categorical, and perceptual metrics, we show that our framework effectively detects emotional inconsistencies, providing insights for improving current dialogue systems. By releasing a systematic evaluation benchmark, we aim to advance emotion-aware spoken dialogue modeling toward more natural and adaptive interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
