Depression Detection Using Video and Speech

Depression is a common mental health disorder affecting millions of individuals worldwide. Timely detection and intervention are crucial for effective treatment and management. This project aims to develop a system for depression detection using facial emotion and speech analysis, leveraging advancements in computer vision and natural language processing. The suggested approach employs cutting-edge machine learning methods to examine speech patterns and facial expressions to look for potential depression indicators. The process of facial emotion analysis entails the extraction of face features from still photos or moving pictures in order to identify important emotions including grief, happiness, and neutral expressions. Speech analysis focuses on removing words, categorizing them as positive or negative, and then using that information to analyze depression. By combining facial emotion and speech analysis, this project aims to develop a non-intrusive and accessible tool for depression detection. The system has the potential to assist mental health professionals in early identification and intervention, improving the overall well-being and quality of life for individuals at risk of depression.