DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-9,Issue-8 ( Aug, 2022 ) | |||||||||
Paper Title |
Utilizing Machine Learning Technique for Emotion Learning and Aiding Mental Health Issues | |||||||||
Author Name |
Nor Azura Husin, Gibson Wan Liang, Nurul Nadhrah Kamaruzaman | |||||||||
Affilition |
1,2,3Faculty of Computer Science, Universiti Putra Malaysia (UPM), Malaysia | |||||||||
Pages |
53-57 | |||||||||
Abstract |
Abstract - 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. Keywords - Mental Health, Emotion Awareness, Machine Learning, Speech Recognition, Emotion Learning. | |||||||||
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