Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition

• URL: http://arxiv.org/abs/2604.14204v1
• Date: Fri, 03 Apr 2026 14:47:26 GMT
• Title: Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
• Abstract summary: Multimodal emotion recognition in conversations aims to infer utterance-level emotions by jointly modeling textual, acoustic, and visual cues within context.<n>We propose a framework that combines dual-space feature disentanglement with dual-branch graph learning.<n> Experiments on IEMOCAP and MELD demonstrate that the proposed method achieves superior performance over strong baselines.
• Score: 37.30058027224552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal emotion recognition in conversations aims to infer utterance-level emotions by jointly modeling textual, acoustic, and visual cues within context. Despite recent progress, key challenges remain, including redundant cross-modal information, imperfect semantic alignment, and insufficient modeling of high-order speaker interactions. To address these issues, we propose a framework that combines dual-space feature disentanglement with dual-branch graph learning. A shared encoder and modality-specific encoders are used to separate modality-invariant and modality-specific representations. The invariant features are modeled by a Fourier graph neural network to capture global consistency and complementary patterns, with a frequency-domain contrastive objective to enhance discriminability. In parallel, a speaker-aware hypergraph is constructed over modality-specific features to model high-order interactions, along with a speaker-consistency constraint to maintain coherent semantics. Finally, the two branches are fused for utterance-level emotion prediction. Experiments on IEMOCAP and MELD demonstrate that the proposed method achieves superior performance over strong baselines, validating its effectiveness.

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