Unveiling Hidden Patterns in Speech: Audio Signal-Based Approach for Depression Detection

Detecting psychological disorders, particularly depression, is a complex and critical task within the realm of mental health assessment. This research explores a novel approach to improve the identification of psychological distresses, such as depression, by highlighting the subjectivity complexity, and biases inherent in traditional diagnostic techniques. Using audio data, and extracting such as voice characteristics and linguistic content from participant interviews, we developed a hybrid model that combines advanced machine learning processing and deep learning approaches. This study investigates the theoretical underpinnings, technical complexities, and practical applications of this model in the context of psychological disorder detection, with the goal of empowering mental health professionals and improving the lives of individuals facing psychological challenges.