Authors - Mouna Meghana Nagala, Anjan Babu G Abstract - Social Anxiety Disorder (SAD) remains one of the most pervasive mental health challenges globally, characterized by a debilitating “perception gap” where individuals consistently overestimate the visibility of their internal distress while underestimating their social performance. This paper introduces an Explainable AI (XAI) multi-modal sensing system designed for automated social anxiety monitoring and self-perception recalibration. The architecture is founded on an event-driven framework integrating real-time threedimensional facial feature encoding (DeepFace), acoustic prosody extraction (Librosa), and Natural Language Processing (NLP) for cognitive distortion detection. The system implements a Cognitive Behavioral Therapy (CBT) logic layer that provides interpretable feedback on linguistic patterns. System performance was benchmarked against the FER-2013 and RAVDESS repositories, yielding an anxiety detection sensitivity of 92.4% and a specificity of 94.7%. The findings affirm that coupling volumetric affective computing with generative AI constitutes a viable pathway toward trustworthy computer-aided detection (CAD) in behavioral health screening programs.
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