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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nesta carta, propomos vetores de recursos eficazes para melhorar o desempenho da detecção de atividade de voz (VAD) empregando uma máquina de vetores de suporte (SVM), que é conhecida por incorporar uma decisão não linear otimizada em duas classes diferentes. Para extrair os vetores de características efetivos, apresentamos um novo esquema que combina os a posteriori SNR, a priori SNR e SNR previsto, amplamente adotados em VAD baseado em modelo estatístico convencional.
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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
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@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},}
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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 -