Gender Recognition from Speech Signal Using CNN, KNN, SVM and RF.

This study focuses on gender recognition from speech, and the gender classification performances of various machine learning methods such as support vector machines (SVM), k-nearest neighbor (KNN), convolutional neural network (CNN), and random forest (RF) are investigated. In the study, the Turkish subsection of the Common Voice dataset are used. From this data set, 3000 speech data are randomly selected with a homogeneous distribution according to age and gender, and 75% of the data is used in training the models and the remaining 25% is used in testing. The first 40 Mel Frequency Cepstral Coefficients (MFCC) obtained from each speech are averaged on the time axis and then applied as input to the classifiers. In the study, after parameter optimization is performed on the training data set for all models with the grid search method, performance evaluations are made on the test set using the optimum parameters. Experimental results show that in gender classification from speech, CNN is the most prominent classifier with 98.67% accuracy, followed by SVM with 97.33%, kNN with 96.8% and RF with 95.73%. [ABSTRACT FROM AUTHOR]