Latent Distribution Decouple for Uncertain-Aware Multimodal Multi-label Emotion Recognition (2025)

Abstract: Multimodal multi-label emotion recognition (MMER) aims to identify the concurrent presence of multiple emotions in multimodal data. Existing studies primarily focus on improving fusion strategies and modeling modality-to-label dependencies. However, they often overlook the impact of aleatoric uncertainty , which is the inherent noise in the multimodal data and hinders the effectiveness of modality fusion by introducing ambiguity into feature representations.To address this issue and effectively model aleatoric uncertainty, this paper proposes Latent emotional Distribution Decomposition with Uncertainty perception (LDDU) framework from a novel perspective of latent emotional space probabilistic modeling. Specifically, we introduce a contrastive disentangled distribution mechanism within the emotion space to model the multimodal data, allowing for the extraction of semantic features and uncertainty. Furthermore, we design an uncertainty-aware fusion multimodal method that accounts for the dispersed distribution of uncertainty and integrates distribution information. Experimental results show that LDDU achieves state-of-the-art performance on the CMU-MOSEI and M ^3 ED datasets, highlighting the importance of uncertainty modeling in MMER. Code is available at https://github.com/201983290498/lddu_mmer.git.

## introduction human interactions convey multiple emotions through various channels: micro-expressions, vocal intonations, and text. multimodal multi-label emotion recognition (mmer) seeks to identify multiple emotions (e.g., happiness, sadness) from multimodal data (e.g., audio, text, and video) @xcite . it could support many downstream applications such as emotion analysis @xcite , human-computer interaction @xcite , and dialogue systems @xcite . ## conclusion we propose lddu, a framework that captures aleatoric uncertainty in mmer through latent emotional space probabilistic modeling. by disentangling semantic features and uncertainty using gaussian distributions, lddu mitigates ambiguity arising from variations in emotional intensity and overlapping emotions. furthermore, an uncertaintyaware fusion module adaptively integrates multimodal features based on their distributional uncertainty. experimental results on cmu-mosei and m 3 ed demonstrate that lddu achieves state-ofthe-art performance. this work pioneers probabilistic emotion space modeling, providing valuable insights into uncertainty-aware affective computing.