Voice pathology detection using machine learning algorithms based on different voice databases
The application of machine learning in analyzing voice disorders has become crucial for non-invasive voice pathology detection using voice signals. However, current systems face challenges such as low detection accuracy, limited databases, and evaluation metrics. More importantly, most existing stud...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025000258 |
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| author | Nurul Mu'azzah Abdul Latiff Fahad Taha Al-Dhief Nurul Fariesya Suhaila Md Sazihan Marina Mat Baki Nik Noordini Nik Abd. Malik Musatafa Abbas Abbood Albadr Ali Hashim Abbas |
| author_facet | Nurul Mu'azzah Abdul Latiff Fahad Taha Al-Dhief Nurul Fariesya Suhaila Md Sazihan Marina Mat Baki Nik Noordini Nik Abd. Malik Musatafa Abbas Abbood Albadr Ali Hashim Abbas |
| author_sort | Nurul Mu'azzah Abdul Latiff |
| collection | DOAJ |
| description | The application of machine learning in analyzing voice disorders has become crucial for non-invasive voice pathology detection using voice signals. However, current systems face challenges such as low detection accuracy, limited databases, and evaluation metrics. More importantly, most existing studies rely on training and testing algorithms based on the same database, limiting their applicability in real-world scenarios with diverse data sources. Unlike traditional approaches that focus solely on single-database training and testing, this study presents a cross-database evaluation strategy to assess the robustness and generalizability of machine learning algorithms for voice pathology detection. Several algorithms, including Online Sequential Extreme Learning Machine (OSELM), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB), were evaluated using two databases: the Saarbrucken Voice Database (SVD) and the Malaysian Voice Pathology Database (MVPD). Two scenarios were considered: (1) training and testing on the same database and (2) training on one database and testing on another. The proposed study uses the Mel-Frequency Cepstral Coefficient (MFCC) technique for extracting features from voices. The algorithms are assessed using many evaluation metrics such as accuracy, precision, sensitivity, specificity, F-measure, and G-mean. Experimental results demonstrate that the OSELM algorithm achieves superior performance across both scenarios, with accuracies of up to 85.71 % in Scenario 1 and 80.77 % in Scenario 2, outperforming other algorithms. This novel approach highlights the reliability of OSELM and the importance of cross-database testing for developing robust and generalizable voice pathology detection systems. |
| format | Article |
| id | doaj-art-aebceeed802748dc80048044c9a280df |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-aebceeed802748dc80048044c9a280df2025-01-23T05:27:39ZengElsevierResults in Engineering2590-12302025-03-0125103937Voice pathology detection using machine learning algorithms based on different voice databasesNurul Mu'azzah Abdul Latiff0Fahad Taha Al-Dhief1Nurul Fariesya Suhaila Md Sazihan2Marina Mat Baki3Nik Noordini Nik Abd. Malik4Musatafa Abbas Abbood Albadr5Ali Hashim Abbas6Faculty of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia; Corresponding author.Faculty of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia; Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaFaculty of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, MalaysiaFaculty of Medicine, Department of Otorhinolaryngology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, MalaysiaFaculty of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, MalaysiaDepartment of Petroleum Project Management, College of Industrial Management of Oil and Gas, Basrah University for Oil and Gas, Al-Basrah, IraqDepartment of Computer Technical Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, IraqThe application of machine learning in analyzing voice disorders has become crucial for non-invasive voice pathology detection using voice signals. However, current systems face challenges such as low detection accuracy, limited databases, and evaluation metrics. More importantly, most existing studies rely on training and testing algorithms based on the same database, limiting their applicability in real-world scenarios with diverse data sources. Unlike traditional approaches that focus solely on single-database training and testing, this study presents a cross-database evaluation strategy to assess the robustness and generalizability of machine learning algorithms for voice pathology detection. Several algorithms, including Online Sequential Extreme Learning Machine (OSELM), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB), were evaluated using two databases: the Saarbrucken Voice Database (SVD) and the Malaysian Voice Pathology Database (MVPD). Two scenarios were considered: (1) training and testing on the same database and (2) training on one database and testing on another. The proposed study uses the Mel-Frequency Cepstral Coefficient (MFCC) technique for extracting features from voices. The algorithms are assessed using many evaluation metrics such as accuracy, precision, sensitivity, specificity, F-measure, and G-mean. Experimental results demonstrate that the OSELM algorithm achieves superior performance across both scenarios, with accuracies of up to 85.71 % in Scenario 1 and 80.77 % in Scenario 2, outperforming other algorithms. This novel approach highlights the reliability of OSELM and the importance of cross-database testing for developing robust and generalizable voice pathology detection systems.http://www.sciencedirect.com/science/article/pii/S2590123025000258Machine learningVoice pathology detectionOSELMSVMDTNB |
| spellingShingle | Nurul Mu'azzah Abdul Latiff Fahad Taha Al-Dhief Nurul Fariesya Suhaila Md Sazihan Marina Mat Baki Nik Noordini Nik Abd. Malik Musatafa Abbas Abbood Albadr Ali Hashim Abbas Voice pathology detection using machine learning algorithms based on different voice databases Results in Engineering Machine learning Voice pathology detection OSELM SVM DT NB |
| title | Voice pathology detection using machine learning algorithms based on different voice databases |
| title_full | Voice pathology detection using machine learning algorithms based on different voice databases |
| title_fullStr | Voice pathology detection using machine learning algorithms based on different voice databases |
| title_full_unstemmed | Voice pathology detection using machine learning algorithms based on different voice databases |
| title_short | Voice pathology detection using machine learning algorithms based on different voice databases |
| title_sort | voice pathology detection using machine learning algorithms based on different voice databases |
| topic | Machine learning Voice pathology detection OSELM SVM DT NB |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025000258 |
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