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

TL;DR

This work reformulates ambiguous emotion recognition as a distributional reasoning problem and presents the first systematic study of ambiguity-aware reasoning in LALMs, comprising 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.

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.

Figures (1)

• Figure 2: Comparison between CE-only (w/o KL supervision) and proposed additional KL supervision. (a) SFT on IEMOCAP, (b) SFT on CREMA-D, (c) DPO on IEMOCAP, and (d) DPO on CREMA-D.