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This paper introduces SYMPHONIA, a novel architecture that enhances multimodal emotion recognition by integrating facial expressions and textual cues through a Dual-Branch Dynamic Attention Mechanism and Hierarchical Adaptive Fusion Module. By leveraging Vision Transformers for facial analysis and RoBERTa embeddings for text, SYMPHONIA achieves state-of-the-art performance across four emotion recognition datasets, including an 80.9% accuracy on IEMOCAP, significantly outperforming existing models like Dualgats and EmoCLIP. The architecture's ability to generalize across datasets, achieving 66.9% accuracy when trained on IEMOCAP and tested on MELD, underscores its robustness in diverse emotional contexts.
SYMPHONIA outperforms top competitors in multimodal emotion recognition by effectively synchronizing facial and textual signals, achieving an impressive 80.9% accuracy on IEMOCAP.
: Human emotions are intricate and difficult to decipher through various modalities. Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteristics of emotional expressions in both visual and textual mediums. This paper presents SYMPHONIA (Synchronizing Facial and Textual Modalities for Emotion Understanding), an innovative architecture engineered to capture and amalgamate emotional signals from facial expressions and language, attuned to contextual and modality interactions. There are two parts to SYMPHONIA: a Facial Emotion Branch that uses Vision Transformers and facial landmarks, and a Textual Emotion Branch that uses RoBERTa embeddings and graph-based reasoning. A Dual-Branch Dynamic Attention Mechanism and a Hierarchical Adaptive Fusion Module are used to connect these branches. SYMPHONIA beat the best models on four datasets: IEMOCAP, MELD, CMU-MOSI, and CMU-MOSEI. It got 80.9% accuracy and 80.1% F1-score on IEMOCAP, which was better than Dualgats (74.8%) and EmoCLIP (75.3%). SYMPHONIA got 74.2% accuracy and 73.5% F1-score for MELD. It beat its competitors by getting a 0.86 Pearson correlation on MOSI and a 0.83 on MOSEI for predicting sentiment. Cross-dataset tests showed that SYMPHONIA could generalize, with 66.9% accuracy when trained on IEMOCAP and tested on MELD. This was better than all the baselines. These results show that SYMPHONIA is good at recognizing emotions and analyzing sentiment in different situations, which shows that it can adapt and do well in different settings.