An Efficient Multimodal Affective Computing Framework For Real-Time Applications

Emotion recognition has become an important area of research in artificial intelligence and affective computing because emotions play a major role in human communication and decision-making. Traditional emotion recognition systems mainly depend on a single type of data such as facial expressions, speech, or text. These unimodal approaches often fail to capture the complexity of human emotions and perform poorly in real-world situations. The present study proposes an optimized and adaptive deep learning framework for real-time multimodal emotion recognition using visual, audio, and textual data. The framework integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), transformer architectures, and attention-based multimodal fusion techniques to improve emotion classification accuracy and contextual understanding. Optimization methods such as model pruning, lightweight architectures, and adaptive learning mechanisms are incorporated to reduce computational complexity and support real-time processing. The study also integrates cultural emotion frameworks such as Rasa Theory to improve contextual and cross-cultural emotional understanding. Experimental observations indicate that multimodal systems outperform unimodal systems in emotion recognition tasks by improving robustness, adaptability, and reliability. The proposed framework contributes to affective computing, healthcare systems, intelligent virtual assistants, and human-computer interaction by providing efficient and scalable real-time emotion recognition solutions.