Comparative Study of Machine Learning Algorithms for Voice based Gender Identification

Abstract

Gender classification is one of the major challenges in the domain of speech analysis now-a-days. Identification of gender from acoustic properties of voice such as mean, median, and frequency, is of high importance. Machine learning is used to solve this problem because it gives promising results for classification techniques. There are several algorithms that can be used to predict the gender using acoustic properties. In this work, classifiers are evaluated using 6 different machine learning algorithms. These algorithms include K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), Support Vector Machine Using Poly kernel and Gradient Boosting (GB). For the same voice sample dataset, the accuracy, recall, precision and F1 Score are computed for all the above-mentioned classifiers. The accuracy obtained from KNN is 96.71, DT is 93.33%, Random Forest is 97.72%, SVM is 98.48%, SVM using Poly Kernel is 97.72%, Gradient Boosting is 96.96%. The predicted values of the considered evaluation metrices on test data for all above classifiers evidenced the supremacy of SVM classifier as it is best fitted model for gender identification of acoustic. SVM attained the accuracy of 98.48% which is the highest among all.