Speaker verification based on adapted Gaussian mixture model feature mapping

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作者：
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China [1 ]
机构：
来源：
Moshi Shibie yu Rengong Zhineng | 2009年 / 3卷 / 417-421期
关键词：
Gaussian distribution - Speech recognition;
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摘要：
To mitigate the channel effect of the handset speaker recognition system, a feature mapping (FM) method is proposed to eliminate the channel variability. Gaussian mixture model (GMM) is used to establish a channel-independent voice model, and the channel-dependent voice models are derived from the GMM using a well-known maximum a posteriori (MAP) adaptation algorithm. The difference of clustering gaussians describes the channel variability for different voice. The mismatch between train and test is compensated by mapping channel rules. Experimental results on NIST99 and 2004 SRE database show that the system performance can be increased by 14.7% and 15.18% by the proposed approach.
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