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Utilizing machine learning technique for emotion learning and aiding mental health issues

Mental health has long been considered a difficult subject to discuss openly, and stigmas still surround it. Poor mental health has become a prevalent issue since people have difficulty talking about and expressing their feelings. The problem has been escalating during this COVID-19 outbreak. In to...

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Main Authors: Husin, Nor Azura, Wan, Gibson Liang, Kamaruzaman, Nurul Nadhrah
Format: Article
Published: Institute of Research and Journals 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102572/
https://iraj.doionline.org/dx/IJAECS-IRAJ-DOIONLINE-18997
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spelling my.upm.eprints.1025722024-02-28T08:47:07Z http://psasir.upm.edu.my/id/eprint/102572/ Utilizing machine learning technique for emotion learning and aiding mental health issues Husin, Nor Azura Wan, Gibson Liang Kamaruzaman, Nurul Nadhrah Mental health has long been considered a difficult subject to discuss openly, and stigmas still surround it. Poor mental health has become a prevalent issue since people have difficulty talking about and expressing their feelings. The problem has been escalating during this COVID-19 outbreak. In today's modern society, it is demonstrated that the technology has the capability of assisting health care providers in assisting their patients with mental health concerns. With this idea, a system named EMOICE, which is a speech emotion recognition system to aid mental health issues, is developed. Doctors or therapists can utilize this technique to analyze and comprehend their patients' emotions, which will aid them in making diagnoses. EMOICE can also be used for emotional learning, where people can use empathy and understanding to deal with mental health concerns. EMOICE will use human speech to extract features such as pitch, voice quality, and voice spectral, which will be used by the algorithm to learn and produce accurate results. EMOICE will employ machine learning techniques, and among the classifiers tested and compared, 1D-Convolutional Neural Network (1D-CNN) has a high accuracy value of 94.78 percent. As a result, this approach can help doctors and therapists better understand their patients' thoughts and emotions, as well as help patients become more self-aware and develop empathy for others in their community and the world around them. Institute of Research and Journals 2022-08 Article PeerReviewed Husin, Nor Azura and Wan, Gibson Liang and Kamaruzaman, Nurul Nadhrah (2022) Utilizing machine learning technique for emotion learning and aiding mental health issues. International Journal of Advances in Electronics and Computer Science, 9 (8). 53- 57. ISSN 2394-2835 https://iraj.doionline.org/dx/IJAECS-IRAJ-DOIONLINE-18997
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Mental health has long been considered a difficult subject to discuss openly, and stigmas still surround it. Poor mental health has become a prevalent issue since people have difficulty talking about and expressing their feelings. The problem has been escalating during this COVID-19 outbreak. In today's modern society, it is demonstrated that the technology has the capability of assisting health care providers in assisting their patients with mental health concerns. With this idea, a system named EMOICE, which is a speech emotion recognition system to aid mental health issues, is developed. Doctors or therapists can utilize this technique to analyze and comprehend their patients' emotions, which will aid them in making diagnoses. EMOICE can also be used for emotional learning, where people can use empathy and understanding to deal with mental health concerns. EMOICE will use human speech to extract features such as pitch, voice quality, and voice spectral, which will be used by the algorithm to learn and produce accurate results. EMOICE will employ machine learning techniques, and among the classifiers tested and compared, 1D-Convolutional Neural Network (1D-CNN) has a high accuracy value of 94.78 percent. As a result, this approach can help doctors and therapists better understand their patients' thoughts and emotions, as well as help patients become more self-aware and develop empathy for others in their community and the world around them.
format Article
author Husin, Nor Azura
Wan, Gibson Liang
Kamaruzaman, Nurul Nadhrah
spellingShingle Husin, Nor Azura
Wan, Gibson Liang
Kamaruzaman, Nurul Nadhrah
Utilizing machine learning technique for emotion learning and aiding mental health issues
author_facet Husin, Nor Azura
Wan, Gibson Liang
Kamaruzaman, Nurul Nadhrah
author_sort Husin, Nor Azura
title Utilizing machine learning technique for emotion learning and aiding mental health issues
title_short Utilizing machine learning technique for emotion learning and aiding mental health issues
title_full Utilizing machine learning technique for emotion learning and aiding mental health issues
title_fullStr Utilizing machine learning technique for emotion learning and aiding mental health issues
title_full_unstemmed Utilizing machine learning technique for emotion learning and aiding mental health issues
title_sort utilizing machine learning technique for emotion learning and aiding mental health issues
publisher Institute of Research and Journals
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102572/
https://iraj.doionline.org/dx/IJAECS-IRAJ-DOIONLINE-18997
_version_ 1792190700258852864
score 13.211869

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