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	Using subband Mel-spectrum centroid and Gaussian mixture correlation for robust speaker identification
	Deng Jing ; Zheng Fang ; Liu Jian ; Wu Wenhu
	2010-05-06 ; 2010-05-06
关键词	Practical/ acoustic signal processing speaker recognition/ subband Mel-spectrum centroid Gaussian mixture correlation robust speaker identification subband amplitude information spectral peak positions additive noise class transition probability matrix/ A4370C Human speech communication A4370F Machine-based speech communication A8736 Speech and biocommunications A4360 Acoustic signal processing
中文摘要	In order to overcome the influence of background noises and improve the robustness of speaker identification systems, two methods were proposed: one is to incorporate subband amplitude information with subband Mel-spectrum centroid (SMSC) because spectral peak positions remain practically unaffected in presence of additive noise. The other is to use a class transition probability matrix to model the high-level information hidden in Gaussian mixture correlation (GMC). Experiments showed that SMSC and GMC could improve the robustness of a speaker identification system in stationary noises, respectively. The average error rate of GMM-UBM system using SMSC and GMC can be reduced by 11.7% compared to conventional GMM-UBM system using MFCC.
语种	中文 ; 中文
出版者	Inst. Acoust. Acad. Sinica ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/10485]
专题	清华大学
推荐引用方式 GB/T 7714	Deng Jing,Zheng Fang,Liu Jian,et al. Using subband Mel-spectrum centroid and Gaussian mixture correlation for robust speaker identification[J],2010, 2010.
APA	Deng Jing,Zheng Fang,Liu Jian,&Wu Wenhu.(2010).Using subband Mel-spectrum centroid and Gaussian mixture correlation for robust speaker identification..
MLA	Deng Jing,et al."Using subband Mel-spectrum centroid and Gaussian mixture correlation for robust speaker identification".(2010).

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