Gender Voiceprint Identification using Machine Learning Algorithms

Voiceprint identification is a popular and highly develop potential technology that is used to identify characteristic of a person or subject. However, selection of classification algorithms behind the voiceprint identification system have to be optimized in order to maximize the performance and capability of the model. In this study, five classification algorithms were presented which are Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF). The performances of these algorithms were evaluated before and after the application of feature extraction technique. The result showed that SVM have the best performance in term of accuracy and AUC either with or without PCA. However, NB was identified as the fastest algorithm in term of computational time. The results also showed that feature extraction techniques have insignificant improvement over the classification of gender voiceprint dataset.