Vision Transformer-Based Audio Analysis for Depression Detection: A Human Factor in Reliable CPS

Cyber-Physical Systems (CPS) are evolving beyond industrial automation to create responsive, human-centric environments that can perceive and adapt to human states. This chapter presents a critical application within this paradigm: the real-time detection of depression through ambient auditory sensing. We propose an AI-driven system that forms a key component of a human-in-the-loop CPS for mental wellness. The system’s physical interface leverages microphones to non-intrusively capture vocal patterns. On the cyber side, a sophisticated signal processing pipeline converts audio into Mel-Frequency Cepstral Coefficients (MFCCs). These features are then fed into an innovative Vision Transformer (ViT) architecture, which excels at identifying subtle, long-range dependencies in the data indicative of depressive states. Validated on the challenging DAIC-WOZ dataset, our model demonstrates state-of-the-art performance with over 96% accuracy. The significance of this research lies in its system-level relevance for CPS. It provides a validated proof-of-concept for technology that can enable continuous, objective, and passive mental health monitoring, paving the way for proactive interventions and truly intelligent assistive systems in clinical and domestic settings.