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@article{189136,
        author = {Varshitha P Alva and Prof. Guruprasanna J K and Sharada Kusuma Rao I G and Smrithi G K and Tharini S Gowda},
        title = {Stress Level and Depression Detection Using Voice and Mantra-Based Healing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {8192-8195},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189136},
        abstract = {This paper presents a deep learning-based system designed to detect stress and depression levels using human voice analysis and provide personalized mantra-based healing recommendations. The proposed model utilizes audio signal processing and neural network architectures such as Wav2Vec2 for stress detection and a Random Forest classifier for depression recognition. Based on the detected condition, the system recommends appropriate mantras that promote relaxation and mental well-being. The approach offers a non-invasive, AI-driven solution combining modern technology with traditional healing practices.},
        keywords = {Stress Detection, Depression Analysis, Wav2Vec2, Deep Learning, Random Forest, Voice Emotion Recognition, Mantra Healing.},
        month = {December},
        }