Speech-Based Mental Health Detection Using Deep Learning

Abstract

Mental health disorders are increasingly prevalent and often remain undiagnosed due to social stigma and limited access to early 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 extracts acoustic and prosodic features, including pitch, tone, intensity, and speech rhythm, to capture emotional and cognitive patterns. Deep learning models such as Convolutional Neural Networks and Recurrent Neural Networks are employed to learn complex temporal and spectral representations from speech data. The system is further enhanced through hybrid learning strategies to improve classification accuracy and robustness. Experimental results indicate that the proposed approach achieves reliable performance in detecting mental health conditions and offers a non-invasive and cost-effective solution for early screening. Additionally, integration with prior research in disease prediction and ensemble learning improves generalization capability. The study demonstrates that speech-based analysis can serve as an effective tool for continuous mental health monitoring, particularly in remote and resource-constrained environments.