Abstract :

Depression Is One Of The Most Prevalent Mental Health Disorders Worldwide And Often Remains Undiagnosed Due To Social Stigma, Lack Of Clinical Resources, And Reliance On Subjective Self-reporting Methods. Early And Accurate Identification Of Depressive Symptoms Is Essential For Timely Intervention And Effective Treatment. With Recent Advancements In Artificial Intelligence And Machine Learning, Automated Systems For Mental Health Assessment Have Gained Significant Attention. Among Various Behavioral Indicators, Speech Characteristics And Facial Expressions Serve As Strong Noninvasive Cues For Detecting Emotional And Psychological States. This Paper Presents An Enhanced Audio-visual Approach For Depression Detection By Analyzing Both Speech Signals And Facial Expression Patterns. The Audio Modality Focuses On Extracting Prosodic, Spectral, And Temporal Features From Speech, Such As Pitch Variation, Energy, Speech Rate, And Voice Quality, Which Are Known To Correlate With Depressive Behavior. The Visual Modality Examines Facial Movements, Micro-expressions, And Emotion Patterns Using Computer Vision And Deep Learning Techniques. By Combining Information From Both Modalities, The System Aims To Improve Detection Accuracy And Robustness Compared To Unimodal Approaches. The Proposed Framework Emphasizes Multimodal Feature Fusion And Machine Learning-based Classification To Identify Depression Severity Levels. Additionally, The Importance Of Interpretability In Healthcare Applications Is Addressed Through The Inclusion Of Explainable AI Techniques, Enabling Transparency In Model Predictions And Increasing Clinical Trust.

Published:

17-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

765-772

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite