Pan, Y., Mirheidari, B., Reuber, M. orcid.org/0000-0002-4104-6705 et al. (3 more authors) (2020) Improving detection of Alzheimer’s Disease using automatic speech recognition to identify high-quality segments for more robust feature extraction. In: Proceedings of Interspeech 2020. Interspeech 2020, 25-29 Oct 2020, Shanghai, China. International Speech Communication Association (ISCA) , pp. 4961-4965.
Abstract
Speech and language based automatic dementia detection is of interest due to it being non-invasive, low-cost and potentially able to aid diagnosis accuracy. The collected data are mostly audio recordings of spoken language and these can be used directly for acoustic-based analysis. To extract linguistic-based information, an automatic speech recognition (ASR) system is used to generate transcriptions. However, the extraction of reliable acoustic features is difficult when the acoustic quality of the data is poor as is the case with DementiaBank, the largest opensource dataset for Alzheimer’s Disease classification. In this paper, we explore how to improve the robustness of the acoustic feature extraction by using time alignment information and confidence scores from the ASR system to identify audio segments of good quality. In addition, we design rhythm-inspired features and combine them with acoustic features. By classifying the combined features with a bidirectional-LSTM attention network, the F-measure improves from 62.15% to 70.75% when only the high-quality segments are used. Finally, we apply the same approach to our previously proposed hierarchical-based network using linguistic-based features and show improvement from 74.37% to 77.25%. By combining the acoustic and linguistic systems, a state-of-the-art 78.34% F-measure is achieved on the DementiaBank task.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 ISCA. Reproduced in accordance with the publisher's self-archiving policy. |
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 EUROPEAN COMMISSION - HORIZON 2020 766287 - TAPAS |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Sep 2021 12:06 |
Last Modified: | 17 Sep 2021 12:06 |
Status: | Published |
Publisher: | International Speech Communication Association (ISCA) |
Refereed: | Yes |
Identification Number: | 10.21437/interspeech.2020-2698 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178304 |