Gender Identification based on Voice Signal Characteristics
Shivam Chaudhary, Davendra Sharma · 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) · 2018
In the present scenario gender identification is based on voice signal of human being. Several Automatic speech recognition systems uses gender identification and has proved to be of great importance. In today's technology gender identification is for speaker's identity in advance security system. In the proposed work, gender identification is done from voice signal by extracting the characteristics such as pitch, energy and mfcc. The features of voice signal is being classified using SVM classifier. Data base includes 280 speech files of which 140 samples are males and 140 samples are females. Training and testing is done on 80% and 20% of data base respectively. The classifier accuracy obtained is 96.45%.