Paper Info
Title
A COMPARATIVE STUDY OF ACOUSTIC AND LINGUISTIC FEATURES CLASSIFICATION FOR ALZHEIMER'S DISEASE DETECTION
Abstract
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 Challenge(1) test set.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414147
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
Alzheimer's Disease detection, ADReSS, features, ASR
Conference
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Jinchao Li101.01
Jianwei Yu2810.92
Zi Ye301.35
Simon Wong413811.68
Man-Wai Mak559469.36
Brian Kan-Wing Mak6708.18
Xunying Liu733052.46
Helen M. Meng81078172.82