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Efficient Multiple Kernel Support Vector Machine Based Voice Activity Detection
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DOI：10.1109/LSP.2011.2159374.png)
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摘要
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In this letter, we propose a multiple kernel support vector machine (MK-SVM) method for multiple feature based VAD. To make the MK-SVM based VAD practical, we adapt the multiple kernel learning (MKL) thought to an efficient cutting-plane structural SVM solver. We further discuss the performances of the MK-SVM with two different optimization objectives, in terms of minimum classification errors (MCE) and improvement of receiver operating characteristic (ROC) curves. Our experimental results show that the proposed method not only leads to better global performances by taking the advantages of multiple features but also has a low computational complexity.
Keyword:
Data fusion
multiple kernel learning
receiver operating characteristic
voice activity detection
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IF:
9.6
论文数:
1.1W
被引数:
1.7W
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Statistical voice activity detection using a multiple observation likelihood ratio test使用多观测似然比测试进行统计语音活动检测
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分享收藏Discriminative Training for Multiple Observation Likelihood Ratio Based Voice Activity Detection
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