Multimodal Emotion Recognition with Cross-Attentive Learning and Feature Fusion

Multi-Modal Emotion Recognition (MMER) plays a crucial role in enhancing human-computer interaction to interpret and respond to human emotions. While existing methods mostly rely on handcrafted features or simple feature concatenation, we introduce a new approach that refines multimodal fusion through cross-attention, enabling the learning of hierarchical dependencies directly from raw data. This allows for more effective interaction between modalities, improving emotion classification. We propose an MMER framework that integrates audio, text, and video modalities, leveraging deep learning models tailored to each data source. A cross-attention mechanism is employed to fuse information across modalities, ensuring the model focuses on the most salient emotional cues. Additionally, focal loss is used to address class imbalance, enhancing recognition of underrepresented emotional states. Evaluated on the IEMOCAP dataset, the proposed model achieves an average accuracy of 88.31% and an F1-score of 76.43%, outperforming existing state-of-the-art methods. These results demonstrate the system’s robustness in recognizing emotions in complex scenarios, highlighting its potential for real-world applications requiring accurate emotion assessment. The source code is available at: https://github.com/alaaNfissi/Mental-Health-Monitoring-MMER.