Cummins, N., Pan, Y., Ren, Z. et al. (8 more authors) (2020) A comparison of acoustic and linguistics methodologies for Alzheimer’s dementia recognition. In: Meng, H., Xu, B. and Zheng, T., (eds.) Interspeech 2020. Interspeech 2020, 25-29 Oct 2020, Shanghai, China. ISCA - International Speech Communication Association , pp. 2182-2186.
Abstract
In the light of the current COVID-19 pandemic, the need for remote digital health assessment tools is greater than ever. This statement is especially pertinent for elderly and vulnerable populations. In this regard, the INTERSPEECH 2020 Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS) Challenge offers competitors the opportunity to develop speech and language-based systems for the task of Alzheimer’s Dementia (AD) recognition. The challenge data consists of speech recordings and their transcripts, the work presented herein is an assessment of different contemporary approaches on these modalities. Specifically, we compared a hierarchical neural network with an attention mechanism trained on linguistic features with three acoustic-based systems: (i) Bag-of-Audio-Words (BoAW) quantising different low-level descriptors, (ii) a Siamese Network trained on log-Mel spectrograms, and (iii) a Convolutional Neural Network (CNN) end-to-end system trained on raw waveforms. Key results indicate the strength of the linguistic approach over the acoustics systems. Our strongest test-set result was achieved using a late fusion combination of BoAW, End-to-End CNN, and hierarchical-attention networks, which outperformed the challenge baseline in both the classification and regression tasks.
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: | © 2020 ISCA. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Alzheimer’s Disease; Bag-of-Audio-Words; Convolutional Neural Network; Siamese Network; Hierarchical Neural Network; Attention Mechanisms |
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 |
Funding Information: | Funder Grant number Medical Research Council N/A |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jan 2021 09:18 |
Last Modified: | 14 Jan 2021 09:18 |
Published Version: | https://www.isca-speech.org/archive/Interspeech_20... |
Status: | Published |
Publisher: | ISCA - International Speech Communication Association |
Refereed: | Yes |
Identification Number: | 10.21437/interspeech.2020-2635 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170037 |