The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam surat ini, kami mencadangkan vektor ciri yang berkesan untuk meningkatkan prestasi pengesanan aktiviti suara (VAD) menggunakan mesin vektor sokongan (SVM), yang diketahui menggabungkan keputusan tak linear yang dioptimumkan ke atas dua kelas berbeza. Untuk mengekstrak vektor ciri yang berkesan, kami membentangkan skema baru yang menggabungkan a posteriori SNR, a priori SNR, dan ramalan SNR, diterima pakai secara meluas dalam VAD berasaskan model statistik konvensional.
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.
Salinan
Q-Haing JO, Yun-Sik PARK, Kye-Hwan LEE, Joon-Hyuk CHANG, "A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 6, pp. 2090-2093, June 2008, doi: 10.1093/ietcom/e91-b.6.2090.
Abstract: In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.6.2090/_p
Salinan
@ARTICLE{e91-b_6_2090,
author={Q-Haing JO, Yun-Sik PARK, Kye-Hwan LEE, Joon-Hyuk CHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors},
year={2008},
volume={E91-B},
number={6},
pages={2090-2093},
abstract={In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.},
keywords={},
doi={10.1093/ietcom/e91-b.6.2090},
ISSN={1745-1345},
month={June},}
Salinan
TY - JOUR
TI - A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors
T2 - IEICE TRANSACTIONS on Communications
SP - 2090
EP - 2093
AU - Q-Haing JO
AU - Yun-Sik PARK
AU - Kye-Hwan LEE
AU - Joon-Hyuk CHANG
PY - 2008
DO - 10.1093/ietcom/e91-b.6.2090
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2008
AB - In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.
ER -