| Paper ID | SPE-28.4 |
| Paper Title |
A COMPARATIVE STUDY OF ACOUSTIC AND LINGUISTIC FEATURES CLASSIFICATION FOR ALZHEIMER’S DISEASE DETECTION |
| Authors |
Jinchao Li, Jianwei Yu, Ye Zi, Simon Wong, The Chinese University of Hong Kong, Hong Kong SAR China; Manwai Mak, The Hong Kong Polytechnic University, Hong Kong SAR China; Brian Mak, The Hong Kong University of Science and Technology, Hong Kong SAR China; Xunying Liu, Helen Meng, The Chinese University of Hong Kong, Hong Kong SAR China |
| Session | SPE-28: Speech Recognition 10: Robustness to Human Speech Variability |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-GASR] General Topics in Speech Recognition |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 Test set. |
