Pan, Y., Mirheidari, B., Reuber, M. orcid.org/0000-0002-4104-6705 et al. (3 more authors) (2019) Automatic hierarchical attention neural network for detecting AD. In: Kubin, G. and Kačič, Z., (eds.) Proceedings of Interspeech 2019. Interspeech 2019, 15-19 Sep 2019, Graz, Austria. International Speech Communication Association (ISCA) , pp. 4105-4109.
Abstract
Picture description tasks are used for the detection of cognitive decline associated with Alzheimer’s disease (AD). Recent years have seen work on automatic AD detection in picture descriptions based on acoustic and word-based analysis of the speech. These methods have shown some success but lack an ability to capture any higher level effects of cognitive decline on the patient’s language. In this paper, we propose a novel model that encompasses both the hierarchical and sequential structure of the description and detect its informative units by attention mechanism. Automatic speech recognition (ASR) and punctuation restoration are used to transcribe and segment the data. Using the DementiaBank database of people with AD as well as healthy controls (HC), we obtain an F-score of 84.43% and 74.37% when using manual and automatic transcripts respectively. We further explore the effect of adding additional data (a total of 33 descriptions collected using a ‘ digital doctor’ ) during model training, and increase the F-score when using ASR transcripts to 76.09%. This outperforms baseline models, including bidirectional LSTM and bidirectional hierarchical neural network without an attention mechanism, and demonstrate that the use of hierarchical models with attention mechanism improves the AD/HC discrimination performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019 ISCA. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Dementia detection; automatic diagnosis; hierarchical attention network; linguistic features |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research The University of Sheffield > Sheffield Teaching Hospitals |
Funding Information: | Funder Grant number Medical Research Council n/a Engineering and Physical Sciences Research Council EP/N027000/1 European Commission - HORIZON 2020 766287 - TAPAS |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Sep 2021 11:10 |
Last Modified: | 17 Sep 2021 11:10 |
Status: | Published |
Publisher: | International Speech Communication Association (ISCA) |
Refereed: | Yes |
Identification Number: | 10.21437/interspeech.2019-1799 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178306 |