Gender recognition of speakers based on MFCC and SVM
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Abstract:
A Chinese speech (mandarin) database was established for speakers gender recognition. A combination method is proposed for gender recognition of speakers based on support vector machine and Melfrequency cepstrum coefficients (MFCC) for classification and feature extraction respectively. The comparative result shows that the accuracy of SVM is 98.7%, which is better than other methods.
