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May 25, 20260 citationsOpen Access

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

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ABAdam Emad Bakr

Key Points

  • The primary aim is to create a unified Arabic speech dataset for early detection of Alzheimer's disease using AI.
  • Develop a comprehensive Arabic speech dataset for Alzheimer’s detection.
  • Implement a dialect-aware speech pipeline to accommodate Arabic linguistic diversity.
  • Address gaps in existing datasets focusing on non-Arabic languages.
  • The proposed framework aims to enhance early diagnostic capabilities for Alzheimer's in Arabic-speaking populations.
  • It seeks to bridge the resource gap by providing essential tools tailored for Arabic language speakers.

Abstract

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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Cite This Study

Adam Emad Bakr (2026) studied this question.

synapsesocial.com/papers/6a13e88c0e02ee3982d333c2https://doi.org/10.5281/zenodo.20355201
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