Comparative Evaluation of Machine Learning Models for Gender Classification from Speech Signals

Abstract

In this study, a machine learning method was developed to determine the gender of people based on voice signals. For this purpose, spectral, temporal, pitch, MFCC (Mel-Frequency Cepstral Coefficients) and energy-based features of the voice data were extracted and analysed. Different machine learning algorithms were compared to determine the model with the highest hit rate. Experimental analyses showed that the ExtraTreesClassifier model performed the best with an accuracy of 98.81%. The accuracy, loss and confusion matrix analyses showed that the model had a high generalisation ability and did not suffer from overfitting. The error rate was calculated to be 1.56% for the female class and 0.79% for the male class. The results of this study suggest that speech-based gender classification systems could contribute to potential applications in areas such as customer service, medical diagnostics and human-computer interaction. © 2025 IEEE.