A voice-based gender identification method using a pitch feature vector and a support vector machine is provided to significantly enhance a gender recognition performance in comparison with a conventional gender identification method using GMM(Gaussian Mixture Model) by using a combination feature vector and applying the support vector machine to voice-based gender identification. A voice-based gender identification method using a pitch feature vector and a support vector machine includes: detecting a voice from a voice database storing male's voice and female's voice; extracting a MFCC(Mel Frequency Cepstral Coefficient) feature vector for training from the detected voice; extracting a training pitch feature vector from the detected voice; combining the training pitch feature vector with the extracted training MFCC feature vector to produce a combination feature vector; and obtaining an optimal weight vector and an optimal bias based on the produced combination feature vector.
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