Gender Classification Based Speaker’s Voice using YIN Algorithm and MFCC

Nowadays, gender classification using voice has been implemented, for a case in the security sector. The voice of each individual has a unique character, because of the diversity in the sound spectrum, frequency, and amplitude between individual. So, the option of technical features to determine specific characteristics to identifying gender came the major issue in classifying. In this study proposed two parameters to determine gender classification based speaker’s voice. There was a fundamental frequency of the YIN algorithm and the cepstrum coefficient of the Mel Frequency Cepstral Coefficient (MFCC) with the classification of Euclidean Distance and Mahalanobis Distance. Based on the data analyzed, it has classified the data test of females on the YIN algorithm with 100% accuracy. Meanwhile, in the male data test, there were several steps to process data that was still misclassified. The first step with the YIN algorithm got an accuracy of 47.5%, then with a combination of YIN and MFCC-Euclidean Distance, the accuracy had raised to 98%. Then continued the Mahalanobis Distance classification to the combination of YIN and MFCC-Euclidean Distance, the accuracy had come 100%. So it can be concluded the parameters applied to determining gender classification based speaker’s voice affect the process and results of the classification.