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Original Article
Dementia and Neurocognitive Disorders 2024: 23: 1: 1-10

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Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer’s Disease Detection
Chan-Young Park , 1 Minsoo Kim,2 YongSoo Shim,3 Nayoung Ryoo,3 Hyunjoo Choi , 4 Ho Tae Jeong , 1 Gihyun Yun,2 Hunboc Lee,2 Hyungryul Kim,2 SangYun Kim , 5 Young Chul Youn 1,6
1 Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea 2 Research and Development, Baikal AI Inc., Seoul, Korea 3 Department of Neurology, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea 4 Department of Communication Disorders, Korea Nazarene University, Cheonan, Korea 5 Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea 6 Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Korea
Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer’s Disease Detection
Chan-Young Park , 1 Minsoo Kim,2 YongSoo Shim,3 Nayoung Ryoo,3 Hyunjoo Choi , 4 Ho Tae Jeong , 1 Gihyun Yun,2 Hunboc Lee,2 Hyungryul Kim,2 SangYun Kim , 5 Young Chul Youn 1,6
1 Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea 2 Research and Development, Baikal AI Inc., Seoul, Korea 3 Department of Neurology, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea 4 Department of Communication Disorders, Korea Nazarene University, Cheonan, Korea 5 Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea 6 Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Korea
Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing
and understanding brain function, especially in the context of cognitive impairment (CI)
and Alzheimer’s disease (AD). This study used voice data to distinguish between normal
cognition and CI or Alzheimer’s disease dementia (ADD).
Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive
decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were
extracted using Mel-frequency cepstral coefficients and Chroma.
Results: A deep neural network (DNN) model showed promising performance, with an
accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of
about 82.0%±1.6% when evaluated against unseen test dataset.
Conclusions: Although results did not demonstrate the level of accuracy necessary for a
definitive clinical tool, they provided a compelling proof-of-concept for the potential use
of voice data in cognitive status assessment. DNN algorithms using voice offer a promising
approach to early detection of AD. They could improve the accuracy and accessibility of
diagnosis, ultimately leading to better outcomes for patients.
Key Words: Voice; Machine Learning; Artificial Intelligence; Alzheimer Disease; Phonetics
대한치매학회지 (Dementia and Neurocognitive Disorders)

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