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A Support Vector Machine-Based Gender Identification Using Speech Signal
Kye-Hwan LEE Sang-Ick KANG Deok-Hwan KIM Joon-Hyuk CHANG
IEICE TRANSACTIONS on Communications
Publication Date: 2008/10/01
Online ISSN: 1745-1345
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Category: Fundamental Theories for Communications
speech signal, gender identification, SVM, GMM, fundamental frequency,
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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.