Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume each utterance has complete modalities, overlooking the common issue of incomplete modalities in real-world scenarios. Recently, graph neural networks (GNNs) have achieved notable results in Incomplete Multimodal Emotion Recognition in Conversations (IMERC). However, traditional GNNs focus on binary relationships between nodes, limiting their ability to capture more complex, higher-order information. Moreover, repeated message passing can cause over-smoothing, reducing their capacity to preserve essential high-frequency details. To address these issues, we propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition. SDR-GNN constructs an utterance semantic interaction graph using a sliding window based on both speaker and context relationships to model emotional dependencies. To capture higher-order and high-frequency information, SDR-GNN utilizes weighted relationship aggregation, ensuring consistent semantic feature extraction across utterances. Additionally, it performs multi-frequency aggregation in the spectral domain, enabling efficient recovery of incomplete modalities by extracting both high- and low-frequency information. Finally, multi-head attention is applied to fuse and optimize features for emotion recognition. Extensive experiments on various real-world datasets demonstrate that our approach is effective in incomplete multimodal learning and outperforms current state-of-the-art methods.

翻译：对话中的多模态情感识别（MERC）旨在利用文本、听觉和视觉模态特征对话语情感进行分类。现有的大多数MERC方法假设每个话语都具有完整的模态，忽视了现实场景中普遍存在的模态不完整问题。近年来，图神经网络（GNNs）在不完整多模态对话情感识别（IMERC）中取得了显著成果。然而，传统GNN侧重于节点间的二元关系，限制了其捕捉更复杂高阶信息的能力。此外，重复的消息传递可能导致过度平滑，削弱其保留关键高频细节的能力。为解决这些问题，本文提出一种用于对话情感识别中不完整多模态学习的谱域重构图神经网络（SDR-GNN）。SDR-GNN基于说话者与上下文关系，采用滑动窗口构建话语语义交互图以建模情感依赖关系。为捕获高阶与高频信息，SDR-GNN采用加权关系聚合机制，确保跨话语的语义特征提取一致性。同时，该方法在谱域进行多频聚合，通过提取高频与低频信息实现不完整模态的高效恢复。最后，应用多头注意力机制对特征进行融合与优化以完成情感识别。在多个真实数据集上的大量实验表明，我们的方法在不完整多模态学习中具有显著效果，且性能优于当前最先进方法。