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• W2155680790 abstract "In large population speaker identification (SI) systems, likelihood computations between an unknown speaker's feature vectors and the registered speaker models can be very time-consuming and impose a bottleneck. For applications requiring fast SI, this is a recognized problem and improvements in efficiency would be beneficial. In this paper, we propose a method whereby GMM-based speaker models are clustered using a simple <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>k</i> -means algorithm. Then, during the test stage, only a small proportion of speaker models in selected clusters are used in the likelihood computations resulting in a significant speed-up with little to no loss in accuracy. In general, as the number of selected clusters is reduced, the identification accuracy decreases; however, this loss can be controlled through proper tradeoff. The proposed method may also be combined with other test stage speed-up techniques resulting in even greater speed-up gains without additional sacrifices in accuracy." @default.
• W2155680790 created "2016-06-24" @default.
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• W2155680790 date "2009-05-01" @default.
• W2155680790 modified "2023-09-25" @default.
• W2155680790 title "Speaker Model Clustering for Efficient Speaker Identification in Large Population Applications" @default.
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• W2155680790 doi "https://doi.org/10.1109/tasl.2008.2010882" @default.
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