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Generative adversarial network-based semi-supervised learning for pathological speech classification

Trinh, Nam orcid logoORCID: 0000-0003-0307-3793 and O'Brien, Darragh (2020) Generative adversarial network-based semi-supervised learning for pathological speech classification. In: 8th International Conference on Statistical Language and Speech Processing (SLSP2020), 14-16 Oct 2020, Cardiff, Wales (Online). ISBN 978-3-030-59430-5

A challenge in applying machine learning algorithms to pathological speech classification is the labelled data shortage problem. Labelled data acquisition often requires significant human effort and time-consuming experimental design. Further, for medical applications, privacy and ethical issues must be addressed where patient data is collected. While labelled data are expensive and scarce, unlabelled data are typically inexpensive and plentiful. In this paper, we propose a semi-supervised learning approach that employs a generative adversarial network to incorporate both labelled and unlabelled data into training. We observe a promising accuracy gain with this approach compared to a baseline convolutional neural network trained only on labelled pathological speech data.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:Conference postponed until 2021 but papers published.
Uncontrolled Keywords:Semi-supervised Learning; Generative Adversarial Networks; Pathological Speech Classification
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Statistical Language and Speech Processing (SLSP2020). Lecture Notes in Computer Science (LNCS) 12379. Springer, Cham. ISBN 978-3-030-59430-5
Publisher:Springer, Cham
Official URL:https://doi.org/10.1007/978-3-030-59430-5_14
Copyright Information:© 2019 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre, School of Computing, Dublin City University under SFI Research Centres Programme (Grant 13/RC/2106), Science Foundation Ireland (SFI) under grant No. 17/RC/PHD/3488, Science Foundation Ireland under grant No. 17/RC/PHD/3488. Refere
ID Code:25067
Deposited On:05 Oct 2020 12:22 by Nam Trinh . Last Modified 05 Oct 2020 12:24

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