AI-Powered Early Detection of Neurological Disorders via Voice Data

Authors

  • Dr. Peter Novak School of Cybersecurity Prague Global Technical University Author

DOI:

https://doi.org/10.63345/

Keywords:

Neurological Disorders, Voice Biomarkers, Early Detection, Speech Analysis, Deep Learning, Natural Language Processing

Abstract

Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis (ALS), multiple sclerosis, and various forms of dementia present an immense public health burden worldwide. These disorders are often diagnosed at advanced stages, when clinical symptoms become pronounced and irreversible neuronal damage has already occurred. Traditional diagnostic pathways rely heavily on neuroimaging, clinical evaluations, and invasive procedures, which are expensive, time-consuming, and frequently inaccessible in low-resource settings. 

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References

https://lh7-us.googleusercontent.com/RbgxJ2bxmLrtS1HPnIalIQpnwl9T5smEy8xgQBT1zQUSuWkF0gYe9rZB6fuCkFpNIDJPY-pxGHA1iKrTZKVX7BYMVvZy0eOBSIJCMisl2HVpp2e4Mar3GdIEamz2rAJ7TX5lAqKfLcR_YqljfmBrm9I

https://www.mdpi.com/ai/ai-06-00068/article_deploy/html/images/ai-06-00068-g001-550.jpg

• Alhanai, T., Au, R., & Ghassemi, M. (2017). Detecting cognitive impairment using speech and language: Machine learning methods and clinical applications. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics, 297–300. https://doi.org/10.1109/BHI.2017.7897215

• Beltrami, D., Gagliardi, G., Rossini Favretti, R., Ghidoni, E., Tamburini, F., & Calza, L. (2018). Speech analysis by natural language processing techniques: A possible tool for early detection of cognitive decline? Frontiers in Aging Neuroscience, 10, 369. https://doi.org/10.3389/fnagi.2018.00369

• Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. F. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10–49. https://doi.org/10.1016/j.specom.2015.03.004

• Eyben, F., Wöllmer, M., & Schuller, B. (2010). Opensmile: The Munich versatile and fast open-source audio feature extractor. Proceedings of the 18th ACM International Conference on Multimedia, 1459–1462. https://doi.org/10.1145/1873951.1874246

• Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2), 407–422. https://doi.org/10.3233/JAD-150520

• González-Fernández, A., & Pulido, M. L. (2019). Voice disorders in Parkinson’s disease: Early markers of neurodegeneration. Parkinsonism & Related Disorders, 63, 32–39. https://doi.org/10.1016/j.parkreldis.2019.02.018

• Haider, F., Dehak, N., Glass, J., & Quatieri, T. F. (2021). Detecting cognitive impairment using paralinguistic acoustic features. Computer Speech & Language, 65, 101133. https://doi.org/10.1016/j.csl.2020.101133

• Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368–376. https://doi.org/10.1136/jnnp.2007.131045

• König, A., Linz, N., Zeghari, R., Klinge, X., & Robert, P. (2018). Detecting Alzheimer’s disease using automatic speech analysis. International Journal of Clinical Neurosciences and Mental Health, 5(1), 1–7. https://doi.org/10.21035/ijcnmh.2018.5.1

• López-de-Ipiña, K., Alonso, J. B., Travieso, C. M., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., ... & Ezeiza, A. (2015). On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors, 13(5), 6730–6745. https://doi.org/10.3390/s130506730

• Luz, S., Haider, F., de la Fuente, S., Fromm, D., & MacWhinney, B. (2021). Alzheimer’s dementia recognition through spontaneous speech: The ADReSS challenge. Computer Speech & Language, 71, 101140. https://doi.org/10.1016/j.csl.2021.101140

• Nasrolahi Afsar, A., Mohammadi, H., & Nahavandi, S. (2022). Artificial intelligence for detecting neurological disorders from speech: A systematic review. IEEE Reviews in Biomedical Engineering, 15, 271–289. https://doi.org/10.1109/RBME.2020.3031772

• Orozco-Arroyave, J. R., Vásquez-Correa, J. C., Klumpp, P., Hönig, F., Arias-Londoño, J. D., & Nöth, E. (2016). Towards an automatic monitoring of the neurological state of Parkinson’s patients from speech. International Journal of Speech Technology, 19, 449–465. https://doi.org/10.1007/s10772-016-9337-x

• Prince, M., Wimo, A., Guerchet, M., Ali, G. C., Wu, Y. T., & Prina, M. (2015). World Alzheimer report 2015: The global impact of dementia. Alzheimer’s Disease International. Retrieved from https://www.alzint.org/resource/world-alzheimer-report-2015/

• Rektorova, I. (2019). Current treatment and research in early Alzheimer’s disease. Journal of Neural Transmission, 126(1), 25–36. https://doi.org/10.1007/s00702-018-01988-3

• Rusz, J., Cmejla, R., Ruzickova, H., & Ruzicka, E. (2011). Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. The Journal of the Acoustical Society of America, 129(1), 350–367. https://doi.org/10.1121/1.3514381

• Shor, J., Jansen, A., Maor, R., Lang, O., Michaely, A., Avigal, M., & Hassidim, A. (2019). Personalizing ASR for pathological speech with limited data. Proceedings of Interspeech 2019, 11–15. https://doi.org/10.21437/Interspeech.2019-1969

• Tóth, L., Gosztolya, G., Grósz, T., Vincze, V., Tóth, Z., & Hoffmann, I. (2018). Automatic detection of mild cognitive impairment from spontaneous speech using ASR. Journal of Biomedical Informatics, 90, 103–111. https://doi.org/10.1016/j.jbi.2018.03.003

• Vásquez-Correa, J. C., Arias-Vergara, T., Orozco-Arroyave, J. R., & Nöth, E. (2020). Multimodal assessment of Parkinson’s disease: A deep learning approach. IEEE Journal of Biomedical and Health Informatics, 24(2), 564–573. https://doi.org/10.1109/JBHI.2019.2895660

• Wroge, T. J., Özkanca, Y., Demiroglu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018). Parkinson’s disease diagnosis using machine learning and voice. Proceedings of the IEEE International Conference on Healthcare Informatics, 356–359. https://doi.org/10.1109/ICHI.2018.00062

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Published

02-09-2025

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Section

Review Article

How to Cite

AI-Powered Early Detection of Neurological Disorders via Voice Data. (2025). Scientific Journal of Artificial Intelligence and Blockchain Technologies, 2(3), Sept(81-89). https://doi.org/10.63345/

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