Feature Selection and Dimension Reduction for Automatic Gender Identification

Gender identification based on speech signal has become gradually a matter of concern in recent years. In this context 6 feature types including MFCC, LPC, RC, LAR, pitch values and formants are compared for automatic gender identification and three best feature types are selected using four feature selection techniques. These techniques are GMM, Decision Tree, Fisher’s Discriminant Ratio, and Volume of Overlap Region. A dimension reduction is done on the best three feature types and the best coefficients are then selected from each feature vector. Selected coefficients are evaluated for gender classification using three types of classifiers including GMM, SVM and MLP neural network. 96.09% gender identification performance was obtained as the best performance using the selected coefficients and MLP classifier.

Keywords:

feature comparison , Gaussian Mixture Model , Decision trees , Fisher’s Discriminant Ratio , Volume of Overlap Region , SVM , MLP