Abstract

In human-computer interaction, emotion detection is essential because it allows computers to comprehend and react to humans' emotional states. In order to improve accuracy and robustness, this work offers a thorough examination into multimodal emotion identification using deep learning techniques. To get a more complex picture of human emotions, the research combines data from many modalities, including voice signals, physiological signals, and facial expressions. Convolutional Neural Networks (CNNs) are used in the suggested method to analyze facial expressions. A well-thought-out multimodal architecture fuses multiple modalities, enabling the model to recognize intricate relationships and patterns across many data sources. Extensive tests are carried out on multimodal emotion recognition benchmark datasets to assess the performance of the proposed technique. The results illustrate how much better the deep learning-based strategy is than more conventional techniques, and how well it can identify and distinguish between a variety of emotional states. Additionally, the model's resilience is evaluated in a range of settings, such as loud settings and culturally diverse contexts. This work offers a cutting-edge method for multimodal emotion identification, which advances the rapidly expanding area of affective computing. In addition to improving emotion identification systems' accuracy, the established framework shows off their potential for use in virtual reality, affective computing, and human-computer interaction. The results of this research open up new possibilities for comprehending and using human emotions in intelligent systems in the future.