Gender identification from tribal speech using several learning techniques

Abstract

Language processing and linguistics researchers are interested in gender identification through audio, as human voices have many distinctive features. Although several gender identification algorithms have been developed, the accuracy and efficiency of the system can still be improved. Despite extensive studies on the topic in various languages, there aren’t many studies on gender identification in the KUI language. Using a variety of machine learning (ML) and deep learning (DL) classifiers, including decision tree (DT), multilayer perceptron (MLP), gradient boosting (GB), linear discriminant analysis (LDA), recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and transformer, the goal of this study is to assess the accuracy of gender identification among diverse KUI language speakers. To verify the effectiveness of the suggested model, several prediction evaluation metrics were calculated, such as the area under the receiver operating characteristic curve (AUC), F1-score, precision, accuracy, and recall. While the findings are compared to other learning models, the gradient-boosting strategy yielded better results with an accuracy rate of 97.0%.