A support vector machine-based gender identification using speech signal

Kye Hwan Lee, Sang Ick Kang, Deok Hwan Kim, Joon Hyuk Chang

Research output: Contribution to journalArticle

11 Scopus citations


We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Original languageEnglish
Pages (from-to)3326-3329
Number of pages4
JournalIEICE Transactions on Communications
Issue number10
StatePublished - 2008 Jan 1



  • Fundamental frequency
  • GMM
  • Gender identification
  • SVM
  • Speech signal

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