TY - GEN
T1 - A comparative study of acoustic and linguistic features classification for Alzheimer’s disease detection
AU - Li, Jinchao
AU - Yu, Jianwei
AU - Ye, Zi
AU - Wong, Simon
AU - Mak, Manwai
AU - Mak, Brian
AU - Liu, Xunying
AU - Meng, Helen
N1 - Funding Information:
This project is partially supported by the HKSARG Research Grants Council’s Theme-based Research Grant Scheme (Project No. T45-407/19N).
Publisher Copyright:
©2021 IEEE
PY - 2021/6
Y1 - 2021/6
N2 - With 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, TF-IDF 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 Challenge1 test set.
AB - With 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, TF-IDF 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 Challenge1 test set.
KW - ADReSS
KW - Alzheimer’s Disease detection
KW - ASR
KW - Features
UR - http://www.scopus.com/inward/record.url?scp=85114965936&partnerID=8YFLogxK
U2 - 10.1109/ICASSP39728.2021.9414147
DO - 10.1109/ICASSP39728.2021.9414147
M3 - Conference article published in proceeding or book
AN - SCOPUS:85114965936
VL - 2021-June
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6423
EP - 6427
BT - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Y2 - 6 June 2021 through 11 June 2021
ER -