AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph Differential Network for Multimodal Emotion Recognition
Abstract
Multimodal dialogue emotion recognition captures emotional cues by fusing text, visual, and audio modalities. However, existing approaches still suffer from notable limitations in modeling emotional dependencies and learning multimodal representations. On the one hand, they are unable to effectively filter out redundant or noisy signals within multimodal features, which hinders the accurate capture of the dynamic evolution of emotional states across and within speakers. On the other hand, during multimodal feature learning, dominant modalities tend to overwhelm the fusion process, thereby suppressing the complementary contributions of non-dominant modalities such as speech and vision, ultimately constraining the overall recognition performance. To address these challenges, we propose an Adaptive Modality-Balanced Dynamic Semantic Graph Differential Network (AMB-DSGDN). Concretely, we first construct modality-specific subgraphs for text, speech, and vision, where each modality contains intra-speaker and inter-speaker graphs to capture both self-continuity and cross-speaker emotional dependencies. On top of these subgraphs, we introduce a differential graph attention mechanism, which computes the discrepancy between two sets of attention maps. By explicitly contrasting these attention distributions, the mechanism cancels out shared noise patterns while retaining modality-specific and context-relevant signals, thereby yielding purer and more discriminative emotional representations. In addition, we design an adaptive modality balancing mechanism, which estimates a dropout probability for each modality according to its relative contribution in emotion modeling.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents AMB-DSGDN, a model that effectively captures dynamic emotional variations in multimodal dialogues through innovative graph-based techniques. Its contributions to the field of emotion recognition are substantial, particularly in addressing noise and modality imbalance, which are critical challenges in multimodal learning.
Methodology
The proposed AMB-DSGDN model introduces a novel approach to multimodal emotion recognition by constructing modality-specific subgraphs that capture both intra- and inter-speaker emotional dependencies. The use of a differential graph attention mechanism to filter out noise and emphasize relevant signals is innovative, as is the adaptive modality balancing mechanism that dynamically adjusts the contribution of each modality. This dual approach addresses significant challenges in the field, such as modality imbalance and noise interference, enhancing the model's ability to capture the dynamic nature of emotional expressions in dialogues.
Results & evaluation
The experiments conducted on the IEMOCAP and MELD datasets demonstrate the model's effectiveness, showing significant performance improvements over state-of-the-art baselines. The use of weighted F1 scores and accuracy as evaluation metrics is appropriate, and the results indicate that the model is robust across different datasets. However, the paper could benefit from more detailed comparisons with baseline models to highlight specific advantages.
Reproducibility
The paper provides a GitHub repository for code access, which is a positive aspect for reproducibility. However, the implementation details could be more comprehensive, particularly regarding hyperparameter settings and training procedures, to facilitate replication of the results by other researchers.
Limitations
Some limitations include potential overfitting due to the complexity of the model and the reliance on specific datasets, which may not generalize well to other contexts or languages. Additionally, the model's performance on the MELD dataset was less impressive, indicating that further refinement may be needed to handle diverse emotional expressions effectively.
Broader impact
The research has significant implications for applications in human-computer interaction, social robotics, and mental health monitoring, where accurate emotion recognition can enhance user experience and system responsiveness. The model's ability to balance multiple modalities dynamically could lead to more empathetic and context-aware AI systems. The paper presents AMB-DSGDN, a model that effectively captures dynamic emotional variations in multimodal dialogues through innovative graph-based techniques. Its contributions to the field of emotion recognition are substantial, particularly in addressing noise and modality imbalance, which are critical challenges in multimodal learning.
Author of this paper? Send a correction.