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Speaker recognition based on discriminant i-vector local distance preserving projection

LI Zhiyi,HE Liang,ZHANG Weiqiang,LIU Jia(Tsinghua National Laboratory for Information Science and Technology,Department of Electronic Engineering, Tsinghua University,Beijing 100084,China)  
The performance of the popular i-vector based speaker recognition system is improved by a manifold learning algorithm named discriminant i-vector local distance preserving projection(DIVLDPP).This algorithm uses the Euclidean distance to measure the i-vector space.The target function minimizes the distance between the same speaker samples and maximizes the distance between neighbouring samples of different speakers.A linear mapping matrix is obtained by solving a generalized eigenvalue problem.Tests on the speaker recognition evaluation data corpus released by the US National Institute of Standards and Technology in 2008 demonstrate that this i-vector system provides better speaker recognition performance.
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