GENDER IDENTIFICATION USING SVM WITH COMBINATION OF MFCC

Gender is an important and most diffrentiative characteristic of a speech. Gender information can also be used to improve the performance of speech and speaker recognition systems. Automatic gender classification is a technique that aims to determine the sex of the speaker through speech signal analysis. However with the increase in biometric security application, practical application of gender identification increased the many fold .The need of gender identification from speech arises several situation such as sorting telephonic call. Many methods of gender identification have been proposed in literature. We implemented the gender classification method and gender dependant feature such as pitch, roll of and energy in combination with MFCC. The clustered approach of above said parameter is implemented using SVM. We also present the experimental result of the proposed approach .It is observed that the accuracy of gender identification system is improved on the basis of size of codebook .The high accuracy is got at 25 codebook size with greater time slice. The accuracy of system tested with respective to gender and age .The efficient recognition rate of 95% is achieved in the age group of 25-30.