A Support Vector Machine-Based Gender Identification Using Speech Signal
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概要
- 論文の詳細を見る
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.
- (社)電子情報通信学会の論文
- 2008-10-01
著者
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Chang Joon-hyuk
School Of Electronic And Electrical Engineering Inha University
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Lee Kye-hwan
School Of Electronic Engineering Inha University
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Kang Sang-ick
School Of Electronic Engineering Inha University
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Kim Deok-hwan
School Of Electronic Engineering Inha University
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Kim Deok-hwan
School Of Electrical Engineering Inha University At Incheon
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