Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA
All individuals are susceptible to experiencing stress in their everyday lives. Nevertheless, stress has a greater influence on females due to both biological and environmental factors. This study utilized female speeches to detect and classify stress and no stress in women. Using speech, composed o...
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| Language: | en |
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International Islamic University Malaysia
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29043/2/02241080420251151411731.pdf http://eprints.utem.edu.my/id/eprint/29043/ https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411/1040 https://doi.org/10.31436/iiumej.v26i1.3411 |
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| author | Asnawi, Ani Liza Zainal, Nur Aishah Ibrahim, Siti Noorjannah Mohamed Azmin, Nor Fadhillah Harum, Norharyati Mat Zin, Nora |
| author_facet | Asnawi, Ani Liza Zainal, Nur Aishah Ibrahim, Siti Noorjannah Mohamed Azmin, Nor Fadhillah Harum, Norharyati Mat Zin, Nora |
| author_sort | Asnawi, Ani Liza |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | All individuals are susceptible to experiencing stress in their everyday lives. Nevertheless, stress has a greater influence on females due to both biological and environmental factors. This study utilized female speeches to detect and classify stress and no stress in women. Using speech, composed of non-invasive and non-intrusive approaches, helps to identify stress better in females. A comparative analysis was conducted between Melfrequency Cepstral Coefficients (MFCCs) and Teager Energy Operator- MFCCs (TEOMFCCs) to determine the best speech feature for classifying emotions associated with stress and no-stress conditions for female voices. With the assistance of the Stress Speech Neural
Network Architecture (SSNNA), an improved accuracy of 93.9% was achieved. This research showed that MFCCs enhanced higher-frequency components in stressed speech,
distinguishing between stress and no-stress classes. This study shows that SSNNA achieved high accuracy with 14 female voices, confirming its ability to function independently of speaker identity. |
| format | Article |
| id | my.utem.eprints-29043 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | International Islamic University Malaysia |
| record_format | eprints |
| spelling | my.utem.eprints-290432025-10-27T07:14:57Z http://eprints.utem.edu.my/id/eprint/29043/ Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA Asnawi, Ani Liza Zainal, Nur Aishah Ibrahim, Siti Noorjannah Mohamed Azmin, Nor Fadhillah Harum, Norharyati Mat Zin, Nora All individuals are susceptible to experiencing stress in their everyday lives. Nevertheless, stress has a greater influence on females due to both biological and environmental factors. This study utilized female speeches to detect and classify stress and no stress in women. Using speech, composed of non-invasive and non-intrusive approaches, helps to identify stress better in females. A comparative analysis was conducted between Melfrequency Cepstral Coefficients (MFCCs) and Teager Energy Operator- MFCCs (TEOMFCCs) to determine the best speech feature for classifying emotions associated with stress and no-stress conditions for female voices. With the assistance of the Stress Speech Neural Network Architecture (SSNNA), an improved accuracy of 93.9% was achieved. This research showed that MFCCs enhanced higher-frequency components in stressed speech, distinguishing between stress and no-stress classes. This study shows that SSNNA achieved high accuracy with 14 female voices, confirming its ability to function independently of speaker identity. International Islamic University Malaysia 2025-01 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29043/2/02241080420251151411731.pdf Asnawi, Ani Liza and Zainal, Nur Aishah and Ibrahim, Siti Noorjannah and Mohamed Azmin, Nor Fadhillah and Harum, Norharyati and Mat Zin, Nora (2025) Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA. IIUM Engineering Journal, Special Issue in Mechanical Engineering, 26 (1). pp. 324-335. ISSN 1511-788X https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411/1040 https://doi.org/10.31436/iiumej.v26i1.3411 |
| spellingShingle | Asnawi, Ani Liza Zainal, Nur Aishah Ibrahim, Siti Noorjannah Mohamed Azmin, Nor Fadhillah Harum, Norharyati Mat Zin, Nora Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title | Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title_full | Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title_fullStr | Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title_full_unstemmed | Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title_short | Utilizing MFCCS and TEO-MFCCS to classify stress in females using SSNNA |
| title_sort | utilizing mfccs and teo-mfccs to classify stress in females using ssnna |
| url | http://eprints.utem.edu.my/id/eprint/29043/2/02241080420251151411731.pdf http://eprints.utem.edu.my/id/eprint/29043/ https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411/1040 https://doi.org/10.31436/iiumej.v26i1.3411 |
| url_provider | http://eprints.utem.edu.my/ |