AI-Powered Emotion Recognition: Exhibiting Human Emotions Through Text, Speech, and Facial Expressions

In the discipline of emotion detection, the capacity to accurately interpret human emotions from diverse data sources, such as text, voice, and facial expressions, is becoming more important. This research aims to create a unified model that combines various complex technologies, including Vision Transformers for visual data, RNN for auditory data, and BERT for textual data. By using a transformer-based design, the proposed system excels at integrating this multimodal data and making first predictions about emotional states. The goal of this technology is to make emotion detection systems more reliable and accurate.