Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.

Journal: Alzheimer's research & therapy
Published Date: Dec 14, 2022

Abstract

BACKGROUND: Alzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more convenient for automatic large-scale Alzheimer's disease detection and has attracted extensive attention from researchers. In particular, deep learning-based speech analysis and language processing techniques for Alzheimer's disease detection have been studied and achieved impressive results.

Authors

  • Qin Yang
    State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China; School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Xinyun Ding
    iFlytek Research, iFlytek Co.Ltd, Hefei, China.
  • Feiyang Xu
    iFlytek Research, iFlytek Co.Ltd, Hefei, China.
  • Zhenhua Ling
    National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China.

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