EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation
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Computer Science > Multimedia
Title:EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation
Abstract:Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC often ignore modality-specific uncertainty across utterances caused by conflicting cues, varying noise, and missing modality-specific signals. We propose EmoEUS, an explicit uncertainty supervision framework for MERC. EmoEUS performs uncertainty-aware multimodal fusion by dynamically weighting modalities using learned variance estimates. We also introduce an explicitly supervised loss that aligns each utterance's predicted variance with the distance between the utterance's distributional representation and its emotion- and modality-specific cluster center. Experiments on IEMOCAP and MELD show that EmoEUS consistently outperforms state-of-the-art methods.
| Comments: | Accept by Interspeech 2026 |
| Subjects: | Multimedia (cs.MM); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.18336 [cs.MM] |
| (or arXiv:2607.18336v1 [cs.MM] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18336
arXiv-issued DOI via DataCite
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