Echoes of the Mind: A CNN Approach for Early Mental Health Prediction

Introduction: The depression often goes undiagnosed due to the absence of objective and accessible detection methodology. This paper focuses on developing an audio-based system that analyzes speech patterns, tone, and sentiment to predict early signs of depression, enabling timely intervention. Objectives: To design a machine learning model that detects depression using speech features such as pitch, tone, and rhythm. To improve early mental health diagnosis by leveraging audio-based sentiment analysis. Methods: Speech signals will be processed using feature extraction techniques like MFCCs (Mel-Frequency Cepstral Coefficients) and spectral analysis. Deep learning models, like LSTM or CNN, will classify speech patterns to identify human behavior. Results: The model accurately distinguishes between sad and neutral speech. Audio-based sentiment analysis demonstrates that it must be a useful tool for early-stage mental health assessments. Conclusion: Depression identification using speech is a non-invasive and scalable option for mental health screening. This method improves early diagnosis of depressive behavior and makes mental health monitoring more accessible and data-driven.