Cross-modal Prompt Disentangled Graph Neural Networks for incomplete conversational emotion recognition

Multimodal emotion recognition relies on the joint modeling of text, speech, and visual signals. In real-world scenarios, however, some modalities are often unavailable due to noise, occlusion, recognition errors, or device malfunction. This not only weakens the use of complementary cross-modal information, but also intensifies imbalance in modality learning. To address this issue, we propose a Cross-modal Prompt Disentangled Graph Neural Network (CPD-GNN) for incomplete multimodal conversational emotion recognition. Specifically, to reduce interference caused by modality heterogeneity, the proposed method first projects incomplete multimodal features into a unified semantic space and disentangles them into shared representations and modality specific representations. On this basis, the framework jointly performs cross-modal information compensation and dynamic modality balancing, providing a robust foundation for modeling both local contextual interactions and higher-order conversational dependencies under missing-modality conditions. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method is effective for incomplete multimodal learning and achieves competitive or superior performance compared with existing approaches.