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Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)

In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic...

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Main Authors: Hashim, Nik Mohd Zarifie, Zahri, Nik Adilah Hanin, Latif, Mohd Juzaila Abd, Hamzah, Rostam Affendi, Hashim, Nik Farizal, Kamal, Maisarah, Sulistiyo, Mahmud Dwi, Kamaruddin, Afiqah Iylia
Format: Article
Published: 2022
Subjects:
R Medicine (General)
RA Public aspects of medicine
Online Access:http://eprints.um.edu.my/43343/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127506958&partnerID=40&md5=1d272ec37272cb3e1505f52d5a22edc2
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spelling my.um.eprints.433432024-11-20T08:27:10Z http://eprints.um.edu.my/43343/ Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN) Hashim, Nik Mohd Zarifie Zahri, Nik Adilah Hanin Latif, Mohd Juzaila Abd Hamzah, Rostam Affendi Hashim, Nik Farizal Kamal, Maisarah Sulistiyo, Mahmud Dwi Kamaruddin, Afiqah Iylia R Medicine (General) RA Public aspects of medicine In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic data for this disease, e.g., heart attack and stroke. The more significant consequence of these two diseases is to disable one or more human capabilities. One of the stroke disease effects is becoming disabled from hearing. Speech disabilities are the focus of this proposed study in this paper. Since the person diagnosed as a stroke patient requires attending the recovery session or rehabilitation session, the rehabilitation center must prepare and provide a sound module and system to help the patient regain their capability. Rehabilitation is an alternative path to gradually giving routine practice to the patient to improve their capability back. For this purpose, the rehab center requires a quantity of time to provide the patient to attend the training session. The training, however, is conducted in two ways, physically and virtually. For the Malaysia stroke patient, the training for pronouncing the vowel in the Malay language is crucial in getting back the speaking capability. Since the Malay language has 6 types of vowels, which are/a/,/e/,/ê/,/i/,/u/, and/o/. Here, there is a limitation to smartly recognizing the difference between the two/e/vowels. Malay's/e/vowel is crucial as the similar spelling vocabulary conveys two different meanings. This study analyzed the differences in recognizing the two/e/vowels using Convolution Neural Network (CNN) with the help of the existing sound-image dataset. © 2022 Little Lion Scientific. All rights reserved. 2022 Article PeerReviewed Hashim, Nik Mohd Zarifie and Zahri, Nik Adilah Hanin and Latif, Mohd Juzaila Abd and Hamzah, Rostam Affendi and Hashim, Nik Farizal and Kamal, Maisarah and Sulistiyo, Mahmud Dwi and Kamaruddin, Afiqah Iylia (2022) Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN). Journal of Theoretical and Applied Information Technology, 100 (5). 1301 – 1318. ISSN 1992-8645, DOI N/A. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127506958&partnerID=40&md5=1d272ec37272cb3e1505f52d5a22edc2 N/A
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine (General)
RA Public aspects of medicine
spellingShingle R Medicine (General)
RA Public aspects of medicine
Hashim, Nik Mohd Zarifie
Zahri, Nik Adilah Hanin
Latif, Mohd Juzaila Abd
Hamzah, Rostam Affendi
Hashim, Nik Farizal
Kamal, Maisarah
Sulistiyo, Mahmud Dwi
Kamaruddin, Afiqah Iylia
Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
description In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic data for this disease, e.g., heart attack and stroke. The more significant consequence of these two diseases is to disable one or more human capabilities. One of the stroke disease effects is becoming disabled from hearing. Speech disabilities are the focus of this proposed study in this paper. Since the person diagnosed as a stroke patient requires attending the recovery session or rehabilitation session, the rehabilitation center must prepare and provide a sound module and system to help the patient regain their capability. Rehabilitation is an alternative path to gradually giving routine practice to the patient to improve their capability back. For this purpose, the rehab center requires a quantity of time to provide the patient to attend the training session. The training, however, is conducted in two ways, physically and virtually. For the Malaysia stroke patient, the training for pronouncing the vowel in the Malay language is crucial in getting back the speaking capability. Since the Malay language has 6 types of vowels, which are/a/,/e/,/ê/,/i/,/u/, and/o/. Here, there is a limitation to smartly recognizing the difference between the two/e/vowels. Malay's/e/vowel is crucial as the similar spelling vocabulary conveys two different meanings. This study analyzed the differences in recognizing the two/e/vowels using Convolution Neural Network (CNN) with the help of the existing sound-image dataset. © 2022 Little Lion Scientific. All rights reserved.
format Article
author Hashim, Nik Mohd Zarifie
Zahri, Nik Adilah Hanin
Latif, Mohd Juzaila Abd
Hamzah, Rostam Affendi
Hashim, Nik Farizal
Kamal, Maisarah
Sulistiyo, Mahmud Dwi
Kamaruddin, Afiqah Iylia
author_facet Hashim, Nik Mohd Zarifie
Zahri, Nik Adilah Hanin
Latif, Mohd Juzaila Abd
Hamzah, Rostam Affendi
Hashim, Nik Farizal
Kamal, Maisarah
Sulistiyo, Mahmud Dwi
Kamaruddin, Afiqah Iylia
author_sort Hashim, Nik Mohd Zarifie
title Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
title_short Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
title_full Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
title_fullStr Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
title_full_unstemmed Analysis on vowel/e/in Malay language recognition via convolution neural network (CNN)
title_sort analysis on vowel/e/in malay language recognition via convolution neural network (cnn)
publishDate 2022
url http://eprints.um.edu.my/43343/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127506958&partnerID=40&md5=1d272ec37272cb3e1505f52d5a22edc2
_version_ 1817841963650514944
score 13.223943

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