Artificial Intelligence Approaches For Multimodal Emotion Understanding

Emotion recognition has become an important research area in artificial intelligence and affective computing. Human emotions are expressed through different modalities such as text, speech, and facial expressions, making multimodal learning essential for accurate emotion detection. This research paper examines transformer-based deep learning models for multimodal emotion recognition and highlights their advantages over traditional machine learning and recurrent neural network approaches. The proposed framework integrates textual, speech, and image data using attention-based fusion strategies to improve contextual understanding and long-range dependency learning. Benchmark datasets such as IEMOCAP, MELD, and CMU-MOSEI are discussed to evaluate the effectiveness of multimodal systems. Experimental analysis indicates that transformer-based architectures outperform conventional CNN and RNN models in terms of recognition accuracy, robustness, and adaptability. The findings suggest that attention mechanisms and multimodal fusion significantly improve emotion recognition performance in real-world applications such as healthcare, education, virtual assistants, and human-computer interaction.