Speech-Based Mental Health Detection Using Deep Learning

Abstract

Mental health disorders are increasingly prevalent and often remain undiagnosed due to social stigma and lack of accessible diagnostic tools. This paper presents a Speech-Based Mental Health Detection system using deep learning techniques to identify psychological conditions such as depression, anxiety, and stress from voice signals. The proposed framework utilizes acoustic and prosodic features, including pitch, tone, energy, and speech patterns, to capture emotional and cognitive states. Deep learning models such as Convolutional Neural Networks and Recurrent Neural Networks are employed to learn complex temporal and spectral patterns in speech data. The system is further enhanced by integrating hybrid learning strategies to improve classification accuracy and robustness. Experimental results demonstrate that the proposed approach achieves reliable performance in detecting mental health conditions and offers a non-invasive, cost-effective solution for early diagnosis. Additionally, the integration of prior research in disease prediction and ensemble learning contributes to improved model generalization. The study highlights the potential of speech-based analysis as an effective tool for continuous mental health monitoring, particularly in remote and resource-limited settings.