Deep Learning Analysis for Early Mental Health Disorder Detection via Voice Data

Mental health disorders such as depression, anxiety, and bipolar disorder significantly affect the well-being of individuals and often go undiagnosed due to reliance on subjective assessments. Voice data, being non-invasive and widely accessible, provides an excellent medium for detecting emotional and cognitive cues associated with mental health conditions. This research investigates the application of deep learning for analyzing vocal features to detect early signs of mental health disorders. Using publicly available datasets and spectrogram-based preprocessing, we evaluate Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. The results demonstrate the effectiveness of deep learning in identifying subtle vocal biomarkers and provide insights into real-time, scalable mental health screening tools.