MFCCs and TEO-MFCCs for stress detection on women gender through deep learning analysis

Nur Aishah, Zainal and Asnawi, Ani Liza and Jusoh, Ahmad Zamani and Ibrahim, Siti Noorjannah and Mohd Ramli, Huda Adibah and Mohamed Azmin, Nor Fadhillah (2023) MFCCs and TEO-MFCCs for stress detection on women gender through deep learning analysis. In: 9th International Conference on Computer and Communication Engineering (ICCCE 2023), 15-16 August 2023, Kuala Lumpur.

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Abstract

Men and women describe differing physical and emotional responses to stress; women reported experiencing it more than men with 11.7% higher. This issue has been affecting women in different ways than men due to biological and social factors (e.g., differences in hormone processes between both genders and dual responsibilities in the workplace as well as at home). This crucial issue raises many concerns about women's mental health, and prolonged stress, such as heart problems, sleep problems, and others, will ideally impact them. Early stress detection is a crucial strategy to overcome the said problems since mental health issues always begin with stress problems. Therefore, in this paper, the MFCCs and TEO-MFCCs for stress detection in the women gender through deep learning are presented. The stress classification had been made by utilizing the speech features, which are Mel Frequency Cepstral Coefficients (MFCCs) and Teager Energy Operator-Mel Frequency Cepstral Coefficients (TEO-MFCCs), with the help of Deep Learning technology, which is Convolutional Neural Networks (CNNs). The Toronto Emotional Speech Set (TESS) has been selected for this study since it consists of women's speech data. The outcome shows that MFCCs provide better accuracy in predicting women's stress, with a 98% score outperformed another study using the same dataset.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	stress detection, speech features, Mel Frequency Cepstral Coefficients (MFCCs), Teager Energy Operator-Mel Frequency Cepstral Coefficients (TEO-MFCCs), Deep Learning, classifiers
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T55.4 Industrial engineering.Management engineering. > T58.5 Information technology T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	DR. Ani Liza Asnawi
Date Deposited:	27 Dec 2023 12:43
Last Modified:	27 Dec 2023 12:43
URI:	http://irep.iium.edu.my/id/eprint/109265

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