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Pathological speech classification using a convolutional neural network

Trinh, Nam and Darragh, O'Brien (2019) Pathological speech classification using a convolutional neural network. In: Irish Machine Vision and Image Processing 2019, 28 - 30 Aug 2019, Dublin, Ireland. ISBN 978-0-9934207-4-0

Convolutional Neural Networks (CNNs) have enabled significant improvements across a number of applications in computer vision such as object detection, face recognition and image classification. An audio signal can be visually represented as a spectrogram that captures the time-varying frequency content of the signal. This paper describes how a CNN can be applied to the spectrogram of an audio signal to distinguish pathological from healthy speech. We propose a CNN structure and implement it using Keras to test the approach. A classification accuracy of over 95% is obtained in experiments on two public pathological speech datasets.
Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Uncontrolled Keywords:Pathological Speech; Audio Classification; Spectrogram; Convolutional Neural Network
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Courtney, Jane, Deegan, Catherine and Leamy, Paul, (eds.) Irish Machine Vision and Image Processing Conference proceedings 2019 IMVIP. . ISBN 978-0-9934207-4-0
Copyright Information:© 2019 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund., Science Foundation Ireland under grant No. 17/RC/PHD/3488.
ID Code:23626
Deposited On:09 Aug 2019 15:09 by Nam Trinh . Last Modified 17 Oct 2019 15:56

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