Approximate Decision Function and Optimization for GMM-UBM Based Speaker Verification
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概要
- 論文の詳細を見る
The GMM-UBM framework has been proved to be one of the most effective approaches to the automatic speaker verification (ASV) task in recent years. In this letter, we first propose an approximate decision function of traditional GMM-UBM, from which it is shown that the contribution to classification of each Gaussian component is equally important. However, research in speaker perception shows that a different speech sound unit defined by Gaussian component makes a different contribution to speaker verification. This motivates us to emphasize some sound units which have discriminability between speakers while de-emphasize the speech sound units which contain little information for speaker verification. Experiments on 2006 NIST SRE core task show that the proposed approach outperforms traditional GMM-UBM approach in classification accuracy.
- （社）電子情報通信学会の論文
- 2009-09-01
著者
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Yan Yonghong
Thinkit Speech Lab. Institute Of Acoustics Chinese Academy Of Sciences
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Yan Yonghong
Institute Of Acoustics Chinese Academy Of Science
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Zhao Qingwei
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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XIAO Xiang
ThinkIT Speech Lab., Institute of Acoustics, Chinese Academy of Sciences
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ZHANG Xiang
ThinkIT Speech Lab., Institute of Acoustics, Chinese Academy of Sciences
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WANG Haipeng
ThinkIT Speech Lab., Institute of Acoustics, Chinese Academy of Sciences
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SUO Hongbin
ThinkIT Speech Lab., Institute of Acoustics, Chinese Academy of Sciences
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Yan Yonghong
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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Yan Yonghong
Thinkit Speech Lab.
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Yan Yonghong
Thinkit Speech Laboratory Institute Of Acoustics Chinese Academy Of Sciences Beijing
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Xiao Xiang
Thinkit Speech Lab. Institute Of Acoustics Chinese Academy Of Sciences
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Suo Hongbin
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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Zhang Xiang
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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Wang Haipeng
Thinkit Speech Lab. Institute Of Acoustics Chinese Academy Of Sciences
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Wang Haipeng
Thinkit Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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Xiao Xiang
Thinkit Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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