Discriminative weight training-based optimally weighted MFCC for gender identification

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differently assigned to each MFCC which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification based on the SVM.