Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction

• URL: http://arxiv.org/abs/2603.08230v1
• Date: Mon, 09 Mar 2026 11:02:34 GMT
• Title: Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction
• Abstract summary: We reformulate ambiguous emotion recognition as a distributional reasoning problem.<n>We present the first systematic study of ambiguity-aware reasoning in LALMs.<n> Experiments on IEMOCAP and CREMA-D demonstrate consistent improvements across SFT, DPO, and GRPO training strategies.
• Score: 23.80840030172631
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguous nature of human emotional expression. Recent large audio-language models show promise in generating richer outputs, but their reasoning ability for ambiguous emotional understanding remains limited. In this work, we reformulate ambiguous emotion recognition as a distributional reasoning problem and present the first systematic study of ambiguity-aware reasoning in LALMs. Our framework comprises two complementary components: an ambiguity-aware objective that aligns predictions with human perceptual distributions, and a structured ambiguity-aware chain-of-thought supervision that guides reasoning over emotional cues. Experiments on IEMOCAP and CREMA-D demonstrate consistent improvements across SFT, DPO, and GRPO training strategies.

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