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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IIUM Press, International Islamic University Malaysia
2025-01-01
|
| Series: | International Islamic University Malaysia Engineering Journal |
| Subjects: | |
| Online Access: | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549932620677120 |
|---|---|
| author | Nur Aishah Zainal Ani Liza Asnawi Siti Noorjannah Ibrahim Nor Fadhillah Mohamed Azmin Norharyati Harum Nora Mat Zin |
| author_facet | Nur Aishah Zainal Ani Liza Asnawi Siti Noorjannah Ibrahim Nor Fadhillah Mohamed Azmin Norharyati Harum Nora Mat Zin |
| author_sort | Nur Aishah Zainal |
| collection | DOAJ |
| 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 Mel-frequency Cepstral Coefficients (MFCCs) and Teager Energy Operator- MFCCs (TEO-MFCCs) 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.
ABSTRAK: Semua individu terdedah kepada stres dalam kehidupan seharian mereka. Walau bagaimanapun, stres memberi pengaruh yang lebih besar terhadap wanita akibat faktor biologi dan persekitaran. Kajian ini menggunakan ucapan untuk mengesan dan mengklasifikasikan stres dan tiada stres dalam kalangan wanita. Penggunaan ucapan, yang merupakan pendekatan tidak invasif dan tidak mengganggu, membantu mengenal pasti tekanan dengan lebih baik dalam kalangan wanita. Analisis perbandingan telah dijalankan antara Mel-frequency Cepstral Coefficients (MFCCs) dan Teager Energy Operator-MFCCs (TEO-MFCCs). Tujuannya adalah untuk menentukan ciri ucapan terbaik bagi mengklasifikasikan emosi yang berkaitan dengan keadaan stres dan tiada stres bagi suara wanita. Dengan bantuan Stress Speech Neural Network Architecture (SSNNA), metrik prestasi yang lebih tinggi dengan ketepatan 93.9% telah dicapai. Penyelidikan ini menunjukkan bahawa MFCCs meningkatkan komponen frekuensi tinggi dalam ucapan yang stres, secara efektif membezakan antara kelas stres dan tiada stres. Kajian ini menunjukkan bahawa SSNNA mencapai ketepatan tinggi dengan 14 suara wanita, mengesahkan ia berfungsi secara bebas daripada identiti penutur.
|
| format | Article |
| id | doaj-art-708e00824fe94d44b32661cac08f4d04 |
| institution | Kabale University |
| issn | 1511-788X 2289-7860 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IIUM Press, International Islamic University Malaysia |
| record_format | Article |
| series | International Islamic University Malaysia Engineering Journal |
| spelling | doaj-art-708e00824fe94d44b32661cac08f4d042025-01-10T12:40:38ZengIIUM Press, International Islamic University MalaysiaInternational Islamic University Malaysia Engineering Journal1511-788X2289-78602025-01-0126110.31436/iiumej.v26i1.3411Utilizing MFCCs and TEO-MFCCs to Classify Stress in Females Using SSNNANur Aishah Zainal0https://orcid.org/0000-0002-3718-374XAni Liza Asnawi1https://orcid.org/0000-0003-1964-5661Siti Noorjannah Ibrahim2https://orcid.org/0000-0002-2892-5959Nor Fadhillah Mohamed Azmin3https://orcid.org/0000-0003-4299-8828Norharyati Harum4https://orcid.org/0000-0003-0068-6025Nora Mat Zin5https://orcid.org/0000-0002-0679-2534International Islamic University Malaysia International Islamic University Malaysia International Islamic University Malaysia International Islamic University Malaysia International Islamic University Malaysia International Islamic University Malaysia 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 Mel-frequency Cepstral Coefficients (MFCCs) and Teager Energy Operator- MFCCs (TEO-MFCCs) 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. ABSTRAK: Semua individu terdedah kepada stres dalam kehidupan seharian mereka. Walau bagaimanapun, stres memberi pengaruh yang lebih besar terhadap wanita akibat faktor biologi dan persekitaran. Kajian ini menggunakan ucapan untuk mengesan dan mengklasifikasikan stres dan tiada stres dalam kalangan wanita. Penggunaan ucapan, yang merupakan pendekatan tidak invasif dan tidak mengganggu, membantu mengenal pasti tekanan dengan lebih baik dalam kalangan wanita. Analisis perbandingan telah dijalankan antara Mel-frequency Cepstral Coefficients (MFCCs) dan Teager Energy Operator-MFCCs (TEO-MFCCs). Tujuannya adalah untuk menentukan ciri ucapan terbaik bagi mengklasifikasikan emosi yang berkaitan dengan keadaan stres dan tiada stres bagi suara wanita. Dengan bantuan Stress Speech Neural Network Architecture (SSNNA), metrik prestasi yang lebih tinggi dengan ketepatan 93.9% telah dicapai. Penyelidikan ini menunjukkan bahawa MFCCs meningkatkan komponen frekuensi tinggi dalam ucapan yang stres, secara efektif membezakan antara kelas stres dan tiada stres. Kajian ini menunjukkan bahawa SSNNA mencapai ketepatan tinggi dengan 14 suara wanita, mengesahkan ia berfungsi secara bebas daripada identiti penutur. https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411stress detection via speechstress classification for femaleMFCCsCNN |
| spellingShingle | Nur Aishah Zainal Ani Liza Asnawi Siti Noorjannah Ibrahim Nor Fadhillah Mohamed Azmin Norharyati Harum Nora Mat Zin Utilizing MFCCs and TEO-MFCCs to Classify Stress in Females Using SSNNA International Islamic University Malaysia Engineering Journal stress detection via speech stress classification for female MFCCs CNN |
| 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 |
| topic | stress detection via speech stress classification for female MFCCs CNN |
| url | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3411 |
| work_keys_str_mv | AT nuraishahzainal utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna AT anilizaasnawi utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna AT sitinoorjannahibrahim utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna AT norfadhillahmohamedazmin utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna AT norharyatiharum utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna AT noramatzin utilizingmfccsandteomfccstoclassifystressinfemalesusingssnna |