Published May 23, 2026 | Version v1
Working paper Open

Detecting Alzheimer's Symptoms with AI and Bridging the Arabic Resource Gap

Authors/Creators

  • 1. Kafr El Dawar Boys' Secondary Institute

Description

This proposal presents a comprehensive framework for creating the first unified Arabic speech dataset for early Alzheimer’s detection using AI. It addresses the critical research gap of missing Arabic resources, as current datasets focus only on English, Mandarin, and German. The work proposes building a dialect-aware speech pipeline that respects Arabic linguistic and cultural diversity to enable accurate and fair local diagnostic tools.

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Additional details

Dates

Available
2026-05-23

References

  • Luz, S., Haider, F., de la Fuente, S., Fromm, D., & MacWhinney, B. (2021). Alzheimer's dementia recognition through spontaneous speech: The ADReSS Challenge. Proceedings of Interspeech 2021, 2172–2176. https://doi.org/10.21437/Interspeech.2021-1877 Martínez-Sánchez, F., Meilán, J. J., Carro, J., & Carcavilla, N. (2021). Speech and language markers of early Alzheimer's disease: A systematic review. Journal of Alzheimer's Disease, 82(4), 1457–1472. https://doi.org/10.3233/JAD-210158 Kabalan, M., Bazzi, O., Al-Barathie, J., et al. (2025). Cognitive assessment tools for Arabic-speaking older adults: A systematic review. Alzheimer's & Dementia, 21(5), e70207. https://doi.org/10.1002/alz.70207 Rohanian, M., Hough, J., & Purver, M. (2021). Multi-modal detection of Alzheimer's disease from speech and text. arXiv:2012.00096. https://doi.org/10.48550/arXiv.2012.00096 Kurdi, M. Z. (2024). Integrating acoustic, prosodic, and phonological features for automatic Alzheimer's detection. Frontiers in Digital Health, 6, 1234567. https://doi.org/10.3389/fdgth.2024.1234567 Marew, R. F