Gender Recognition by Voice using Machine Learning Techniques
##plugins.themes.academic_pro.article.sidebar##
##plugins.themes.academic_pro.article.main##
Abstract
Gender Recognition using voice is of enormous prominence in the near future technology as its uses could range from smart assistance robots to customer service sector and many more. Machine learning (ML) models play a vital role in achieving this task. Using the acoustic properties of voice, different ML models classify the gender as male and female. In this research we have used the ML models- Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbor (KNN), and ensemble method (KNN, logistic regression, SVM). To propose which algorithm is best for recognizing gender, we have evaluated the models based on results achieved from analysis of accuracy, recall, F1 score, and precision.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- B ̈uy ̈ukyılmaz, M. and C ̧ ıbıkdiken, A. 2016. Voice gender recognition using deep learning. In Conference: Modeling, Simulation and Optimization Technologies and Applications. DOI: https://doi.org/10.2991/msota-16.2016.90
- Harb, H. and Chen, L. 2007. A general audio classifier based on human perception motivated model. Multimedia Tools Appl. 34, 3 (09), 375–395. DOI: https://doi.org/10.1007/s11042-007-0108-9
- Livieris, I., Pintelas, E., and Pintelas, P. 2019. Gender recognition by voice using an improved self-labeled algorithm. Machine Learning and Knowledge Extraction Vol.1, 492–503. DOI: https://doi.org/10.3390/make1010030
- Mridula Korde, Abhishek Joshi, A. S. 2022. Human recognition model using cnn. Inter-national Journal of Next-Generation Computing (IJNGC) 13. DOI: https://doi.org/10.47164/ijngc.v13i5.861
- Zvarevashe, K. and Olugbara, O. O. 2018. Gender voice recognition using random forest recursive feature elimination with gradient boosting machines. 2018 International Confer-ence on Advances in Big Data, Computing and Data Communication Systems (icABCD),1–6. DOI: https://doi.org/10.1109/ICABCD.2018.8465466