Almomani, A, Alomoush, W, Gupta, B B, Alweshah, Mohammed, Alauthman, Mohammad, Jabai, Aseel, Hamad, Ghufran, Abbass, Anwar and Abdalla, Meral (2023) Age and Gender Classification Using Backpropagation and Bagging Algorithms. Computers, Materials & Continua, 74 (2). pp. 3045-3062. ISSN 1546-2226
v74n2/50216 - Published Version
Download (68kB)
Abstract
Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.
Affiliation: | Skyline University College |
---|---|
SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114, Alomoush, W ORCID: https://orcid.org/0000-0002-2937-4327 and Gupta, B B |
All Author(s): | Almomani, A, Alomoush, W, Gupta, B B, Alweshah, Mohammed, Alauthman, Mohammad, Jabai, Aseel, Hamad, Ghufran, Abbass, Anwar and Abdalla, Meral |
Item Type: | Article |
Uncontrolled Keywords: | Classify voice gender; accent; age; bagging algorithms; back propagation algorithms; AI classifiers |
Subjects: | B Information Technology > BJ Computer Science B Information Technology > BM Artificial Intelligence |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 22 Dec 2023 09:14 |
Last Modified: | 22 Dec 2023 09:14 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/760 |
Publisher URL: | https://doi.org/10.32604/cmc.2023.030567 |
Publisher OA policy: | |
Related URLs: |
|
Actions (login required)
Statistics for this ePrint Item |