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.
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