Using a Kind of Novel Phonotactic Information for SVM Based Speaker Recognition
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概要
- 論文の詳細を見る
In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.
- （社）電子情報通信学会の論文
- 2009-04-01
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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ZHANG 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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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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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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