IEICE TRANSACTIONS on Communications
A Support Vector Machine-Based Gender Identification Using Speech Signal
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.10 pp.3326-3329
- Publication Date
- 2008/10/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.10.3326
- Type of Manuscript
- LETTER
- Category
- Fundamental Theories for Communications
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Kye-Hwan LEE, Sang-Ick KANG, Deok-Hwan KIM, Joon-Hyuk CHANG, "A Support Vector Machine-Based Gender Identification Using Speech Signal" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 10, pp. 3326-3329, October 2008, doi: 10.1093/ietcom/e91-b.10.3326.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.10.3326/_p
Copy
@ARTICLE{e91-b_10_3326,
author={Kye-Hwan LEE, Sang-Ick KANG, Deok-Hwan KIM, Joon-Hyuk CHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Support Vector Machine-Based Gender Identification Using Speech Signal},
year={2008},
volume={E91-B},
number={10},
pages={3326-3329},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e91-b.10.3326},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - A Support Vector Machine-Based Gender Identification Using Speech Signal
T2 - IEICE TRANSACTIONS on Communications
SP - 3326
EP - 3329
AU - Kye-Hwan LEE
AU - Sang-Ick KANG
AU - Deok-Hwan KIM
AU - Joon-Hyuk CHANG
PY - 2008
DO - 10.1093/ietcom/e91-b.10.3326
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2008
AB - 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.
ER -
English
