A comparative study of acoustic and linguistic features classification for Alzheimer’s disease detection
Refereed conference paper presented and published in conference proceedings


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AbstractWith the global population ageing rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset followed by gradual, irreversible deterioration in cognitive domains (memory, communication, etc). Thus the detection of Alzheimer's disease is crucial for timely intervention to slow down disease progression. This paper presents a comparative study of different acoustic and linguistic features for the AD detection using various classifiers. Experimental results on ADReSS dataset reflect that the proposed models using ComParE, X-vector, Linguistics, TFIDF and BERT features are able to detect AD with high accuracy and sensitivity, and are comparable with the state-of-the-art results reported. While most previous work used manual transcripts, our results also indicate that similar or even better performance could be obtained using automatically recognized transcripts over manually collected ones. This work achieves accuracy scores at 0.67 for acoustic features and 0.88 for linguistic features on either manual or ASR transcripts on the ADReSS Challenge 1 test set.
Acceptance Date09/02/2021
All Author(s) ListJinchao Li, Jianwei Yu, Ye Zi, Simon Wong, Manwai Mak, Brian Mak, Xunying Liu, Helen Meng
Name of Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Start Date of Conference06/06/2021
End Date of Conference11/06/2021
Place of ConferenceToronto
Country/Region of ConferenceCanada
Proceedings Title2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Year2021
PublisherIEEE
Pages6423 - 6427
LanguagesEnglish-United States

Last updated on 2024-16-04 at 00:16