Pan, Y., Mirheidari, B., Tu, Z. et al. (6 more authors) (2020) Acoustic feature extraction with interpretable deep neural network for neurodegenerative related disorder classification. In: Proceedings of Interspeech 2020. Interspeech 2020, 25-29 Oct 2020, Shanghai, China. International Speech Communication Association (ISCA) , pp. 4806-4810.
Abstract
Speech-based automatic approaches for detecting neurodegenerative disorders (ND) and mild cognitive impairment (MCI) have received more attention recently due to being non-invasive and potentially more sensitive than current pen-and-paper tests. The performance of such systems is highly dependent on the choice of features in the classification pipeline. In particular for acoustic features, arriving at a consensus for a best feature set has proven challenging. This paper explores using deep neural network for extracting features directly from the speech signal as a solution to this. Compared with hand-crafted features, more information is present in the raw waveform, but the feature extraction process becomes more complex and less interpretable which is often undesirable in medical domains. Using a SincNet as a first layer allows for some analysis of learned features. We propose and evaluate the Sinc-CLA (with SincNet, Convolutional, Long Short-Term Memory and Attention layers) as a task-driven acoustic feature extractor for classifying MCI, ND and healthy controls (HC). Experiments are carried out on an in-house dataset. Compared with the popular hand-crafted feature sets, the learned task-driven features achieve a superior classification accuracy. The filters of the SincNet is inspected and acoustic differences between HC, MCI and ND are found.
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 12:26 |
Last Modified: | 17 Sep 2021 12:26 |
Status: | Published |
Publisher: | International Speech Communication Association (ISCA) |
Refereed: | Yes |
Identification Number: | 10.21437/Interspeech.2020-2684 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178305 |