EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems

Liu, Jingwen, Cheng, Kan Jen, Lian, Jiachen, Anand, Akshay, Jain, Rishi, Qiao, Faith, Netzorg, Robin, Chou, Huang-Cheng, Li, Tingle, Lin, Guan-Ting, Anumanchipalli, Gopala

arXiv.org Artificial Intelligence 

--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. T o 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. Spoken communication relies on more than just the words we use.

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