Real-Time Adaptive Multimodal Emotion Recognition Using Attention-Based Deep Learning Framework

Emotion recognition has surely become an important part in making smart systems that can interact with humans. Moreover, this technology helps computers understand human feelings better. Human feelings are actually complex and people definitely show them through face expressions, speaking, and writing. Basically, traditional single-mode methods cannot handle the same complexity in an effective way. This paper surely shows how to build a system that can recognize human emotions in real-time using multiple inputs and smart computer learning methods. Moreover, the system uses attention-based techniques to adapt and improve its understanding of different emotional states. The proposed system combines visual, audio, and text data to further improve accuracy and make the system itself more robust. As per current research, advanced computer models like CNN, RNN, and transformer systems are used for finding key features, while attention methods help combine different types of data efficiently. Regarding the process,