Gender Recognition by Voice using Machine Learning Techniques


Sweta Jain
Neha Pandey
Vaidehi Choudhari
Pratik Yawalkar
Amey Admane


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.


How to Cite
Sweta Jain, Neha Pandey, Vaidehi Choudhari, Pratik Yawalkar, & Amey Admane. (2023). Gender Recognition by Voice using Machine Learning Techniques. International Journal of Next-Generation Computing, 14(1).


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