This is not the latest version of this item. The latest version can be found here.
Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity
Name
2009.00700.pdf
Description
Accepted version
Size
414.47 KB
Format
Adobe PDF
Checksum (MD5)
c6b422f229a187a5c9f22f55ebd66a51
Author(s)
Sarawgi, U
Zulfikar, W
Soliman, N
Maes, P
Date Issued
October 2020
Journal
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher
ISCA
Citation
Sarawgi, U, Zulfikar, W, Soliman, N and Maes, P. 2020. "Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity." Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2020-October.
Version
Original manuscript
Abstract
Copyright © 2020 ISCA Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.21437/Interspeech.2020-3137
