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

Abstract: 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.

Mental Health, Deep learning, Voice data, early detection

1. https://www.sciencedirect.com/science/article/pii/S2352914823001284 [Google Scholar] [Crossref]

2. https://pmc.ncbi.nlm.nih.gov/articles/PMC7293215/ [Google Scholar] [Crossref]

3. https://ijisae.org/index.php/IJISAE/article/view/5561 [Google Scholar] [Crossref]

4. https://clinical-practice-and-epidemiology-in-mental-health.com/VOLUME/20/ELOCATOR [Google Scholar] [Crossref]

5. /e17450179315688/FULLTEXT/ [Google Scholar] [Crossref]

6. https://www.researchgate.net/publication/391707310_Early_detection_of_mental_health disorders_using_machine_learning_models_using_behavioral_and_voice_data_analysis [Google Scholar] [Crossref]

7. Merino, M. et al. Body perceptions and psychological well-being: A review of the impact of social media and physical measurementson self-esteem and mental health with a focus on body image satisfaction and its relationship with cultural and gender factorsHealthcare 12(14), 1396 (2024). [Google Scholar] [Crossref]

8. Chen, X. & Pan, Z. A convenient and low-cost model of depression screening and early warning based on voice data using forpublic mental health. Int. J. Environ. Res. Public Health 18(12), 6441 (2021). [Google Scholar] [Crossref]

9. Pourkeyvan, A., Safa, R. &Sorourkhah, A. Harnessing the power of hugging face transformers for predicting mental healthdisorders in social networks. IEEE Access 12, 28025–28035 (2024). [Google Scholar] [Crossref]

10. Khan, S. & Alqahtani, S. Hybrid machine learning models to detect signs of depression. Multimed. Tools Appl. 83(13), 38819–38837 (2024). [Google Scholar] [Crossref]

11. Ku, W. L. & Min, H. Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective ResponseErrors. Healthcare 12(6), 625 (2024). [Google Scholar] [Crossref]

12. RajuKanchapogu, N., & Mohanty, S. N. Enhancing Depression Predictive Models: A Comparative Study of Hybrid Ai, MachineLearning and Deep Learning Techniques. (2024). [Google Scholar] [Crossref]

13. Zhou, H., Zhou, F., Zhao, C., Xu, Y., Luo, L., & Chen, H. Multimodal data integration for precision oncology: Challenges and futuredirections. arXiv preprint arXiv:2406.19611. (2024) [Google Scholar] [Crossref]

14. Almutairi, S. et al. A Hybrid Deep Learning Model for Predicting Depression Symptoms from Large-Scale Textual Dataset. IEEEAccess https://doi.org/10.1109/ACCESS.2024.3496741 (2024). [Google Scholar] [Crossref]

15. Mahmood, T., Rehman, A., Saba, T., Nadeem, L. & Bahaj, S. A. O. Recent advancements and future prospects in active deeplearning for medical image segmentation and classification. IEEE Access 11, 113623–113652 (2023). [Google Scholar] [Crossref]

16. Obaido, G. et al. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, andprospects. Mach. Learn. Appl. 17, 100576 (2024). [Google Scholar] [Crossref]

17. Mohajeri, M., Towsyfyan, N., Tayim, N., Faroji, B. B. & Davoudi, M. Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study. Psychiatr. Q. https://doi.org /10.1007/s11126-024-10101-x (2024). [Google Scholar] [Crossref]

18. Di Cesare, M. G., Perpetuini, D., Cardone, D. & Merla, A. Assessment of Voice Disorders Using Machine Learning and VocalAnalysis of Voice Samples Recorded through Smartphones. BioMedInformatics4(1), 549–565 (2024). [Google Scholar] [Crossref]

19. Cheong, I., Caliskan, A. & Kohno, T. Safeguarding human values: rethinking US law for generative AI’s societal impacts. AI Eth.https://doi.org/10.1007/s43681-024-00451-4 (2024). [Google Scholar] [Crossref]

20. Zafar, A. Balancing the scale: Navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices. Discov. Artif. Intell. 4(1), 27 (2024). [Google Scholar] [Crossref]

21. Al-Tameemi, I. K. S., Feizi-Derakhshi, M. R., Pashazadeh, S. & Asadpour, M. Interpretable multimodal sentiment classificationusing deep multi-view attentive network of image and text data. IEEE Access 11, 91060–91081 (2023). [Google Scholar] [Crossref]

22. Javed, H., Muqeet, H. A., Javed, T., Rehman, A. U. & Sadiq, R. Ethical Frameworks for Machine Learning in Sensitive HealthcareApplications. IEEE Access. 12, 16233–16254 (2023). [Google Scholar] [Crossref]

23. Zhang, Z. Early warning model of adolescent mental health based on big data and machine learning. Soft. Comput. 28(1), 811–828(2024). [Google Scholar] [Crossref]

24. Satapathy, S. K., Patel, V., Gandhi, M., & Mohapatra, R. K. Comparative Study of Brain Signals for Early Detection of Sleep Disorder [Google Scholar] [Crossref]

25. Using Machine and Deep Learning Algorithm. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technologyand Management for Social Innovation (IATMSI) (Vol. 2, pp. 1–6). IEEE. (2024) [Google Scholar] [Crossref]

26. Hossain, S., Umer, S., Rout, R. K. & Al Marzouqi, H. A Deep Quantum Convolutional Neural Network Based Facial Expression Recognition for Mental Health Analysis. IEEE Trans. Neural Syst. Rehabil. Eng. https://doi.org/10.1109/TNSRE.2024.3385336(2024). [Google Scholar] [Crossref]

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