Mirheidari, B., Pan, Y., Blackburn, D. et al. (5 more authors) (Submitted: 2020) Data augmentation using generative networks to identify dementia. arXiv. (Submitted)
Abstract
Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). For reuse permissions, please contact the Author(s). |
Keywords: | clinical applications of speech technology; sparse data; automatic speech recognition; data augmentation |
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 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Apr 2020 09:25 |
Last Modified: | 29 Apr 2020 10:12 |
Published Version: | https://arxiv.org/abs/2004.05989 |
Status: | Submitted |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160047 |