Enhancing Emotion Detection Accuracy and Transparency through Multimodal Fusion and Explainable AI

In the absence of proper accuracy and transparency in emotion detection systems, this research proposes an advanced Multimodal Emotion recognition framework by using Explainable AI (XAI). A majority of the conventional emotion detection models are based on single modal data (text, audio or video) which constrains their exploration of the entire spectrum of human emotions. Since this may be challenging, this system uses a multimodal fusion approach in combining text, audio, and video inputs for higher prediction accuracy. In this work, incorporating XAI techniques are LIME, SHAP, and Grad CAM to make the proposed model more interpretable, giving human comprehensible reasons for the model’s decisions. Shows that the proposed system is more accurate than single modal models and at the same time ensures fairness, reduces bias and increases trust when doing emotion classification. Such a framework can have applications in mental health assessment, human computer interaction and customer service, as accuracy and explainability are crucial.