Vowel’s classification for stroke patients through rehabilitation performance via image-profiled sound data
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Author |
Nur Syahmina Ahmad Azhar, Nik Mohd. Zarifie Hashim, Afiqah Iylia Kamaruddin, Nik Adilah Hanin Zahri and Mahmud Dwi Sulistiyo
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e-ISSN |
1819-6608 |
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On Pages
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1411-1424
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Volume No. |
18
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Issue No. |
12
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Issue Date |
August 30, 2023
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DOI |
https://doi.org/10.59018/0623179
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Keywords |
convolutional neural network (CNN), image-profiled sound, rehabilitation, spectrogram image, stroke patients, vowel recognition.
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Abstract
In terms of medicine, a disorder is a disturbance of the mind or body's normal functioning. Communication issues
may result from stroke because it damages the parts of the brain that control language. The capacity to talk, read, write, and
comprehend speeches can all be affected after a stroke. The rehabilitation and treatment of patients generally take a long
time and include constant medication, exercise, and rehabilitation training. However, most rehab facilities throughout the
world still manually carry out this rehabilitation process. Machine learning and deep learning have been introduced to this
medical field to aid rehabilitation using the new technology due to computer vision's impact on this field. A reliable
Convolution Neural Network with a graphical user interface is introduced in this study to support and enhance
rehabilitation efforts. The spectrogram in the image-profiled sound is used to provide the optimum outcome and accuracy.
This project aims to develop a neural network that can distinguish vowels between a normal person's and stroke patients'
voices. In this proposed paper's result, an intelligent Convolution Neural Network system for Malay language vowel
detection with high-performance accuracy is demonstrated with maximum accuracy was 92.96% with 20 epoch numbers
and 6 batch size. This outcome showed that the proposed method, even using a simple network design, is still competitive
compared with other methods. The proposed method is ideal for training and validating vowel recognition accuracy
especially for stroke patients.
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