A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach
Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/11/1750 |
| _version_ | 1797493697145733120 |
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| author | Eman H. Alkhammash Myriam Hadjouni Ahmed M. Elshewey |
| author_facet | Eman H. Alkhammash Myriam Hadjouni Ahmed M. Elshewey |
| author_sort | Eman H. Alkhammash |
| collection | DOAJ |
| description | Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble learning with a variety of machine learning models in order to create more accurate classifiers. In this paper, a stacked ensemble for gender voice recognition model is presented, using four classifiers, namely, k-nearest neighbor (KNN), support vector machine (SVM), stochastic gradient descent (SGD), and logistic regression (LR) as base classifiers and linear discriminant analysis (LDA) as meta classifier. The dataset used includes 3168 instances and 21 features, where 20 features are the predictors, and one feature is the target. Several prediction evaluation metrics, including precision, accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), were computed to verify the execution of the proposed model. The results obtained illustrated that the stacked model achieved better results compared to other conventional machine learning models. The stacked model achieved high accuracy with 99.64%. |
| first_indexed | 2024-03-10T01:23:45Z |
| format | Article |
| id | doaj.art-522f411b18b544bbab6b12519fcb5d68 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:23:45Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-522f411b18b544bbab6b12519fcb5d682023-11-23T13:55:17ZengMDPI AGElectronics2079-92922022-05-011111175010.3390/electronics11111750A Hybrid Ensemble Stacking Model for Gender Voice Recognition ApproachEman H. Alkhammash0Myriam Hadjouni1Ahmed M. Elshewey2Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaComputer Science Department, Faculty of Computers and Information, Suez University, Suez, EgyptGender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble learning with a variety of machine learning models in order to create more accurate classifiers. In this paper, a stacked ensemble for gender voice recognition model is presented, using four classifiers, namely, k-nearest neighbor (KNN), support vector machine (SVM), stochastic gradient descent (SGD), and logistic regression (LR) as base classifiers and linear discriminant analysis (LDA) as meta classifier. The dataset used includes 3168 instances and 21 features, where 20 features are the predictors, and one feature is the target. Several prediction evaluation metrics, including precision, accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), were computed to verify the execution of the proposed model. The results obtained illustrated that the stacked model achieved better results compared to other conventional machine learning models. The stacked model achieved high accuracy with 99.64%.https://www.mdpi.com/2079-9292/11/11/1750machine learningstacking modelensemble learningk-nearest neighborstochastic gradient descentsupport vector machine |
| spellingShingle | Eman H. Alkhammash Myriam Hadjouni Ahmed M. Elshewey A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach Electronics machine learning stacking model ensemble learning k-nearest neighbor stochastic gradient descent support vector machine |
| title | A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach |
| title_full | A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach |
| title_fullStr | A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach |
| title_full_unstemmed | A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach |
| title_short | A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach |
| title_sort | hybrid ensemble stacking model for gender voice recognition approach |
| topic | machine learning stacking model ensemble learning k-nearest neighbor stochastic gradient descent support vector machine |
| url | https://www.mdpi.com/2079-9292/11/11/1750 |
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