XU Longting, YANG Zhen, SUN Linhui. Simplification of I-Vector Extraction for Speaker Identification[J]. Chinese Journal of Electronics, 2016, 25(6): 1121-1126. doi: 10.1049/cje.2016.10.016
Citation: XU Longting, YANG Zhen, SUN Linhui. Simplification of I-Vector Extraction for Speaker Identification[J]. Chinese Journal of Electronics, 2016, 25(6): 1121-1126. doi: 10.1049/cje.2016.10.016

Simplification of I-Vector Extraction for Speaker Identification

doi: 10.1049/cje.2016.10.016
Funds:  This work is supported by the National Natural Science Foundation of China (No.60971129, No.61271335, No.61501251), the Scientific Innovation Research Programs of College Graduate in Jiangsu Province (No.CXZZ13_0488), Key Laboratory of the Ministry of Public Security Smart Speech Technology (No.2014ISTKFKT02), the Natural Science Foundation of Jiangsu Province (No.BK20140891), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.13KJB510020), and the Science Foundation of Nanjing University of Posts and Telecommunications (No.NY214191).
  • Received Date: 2015-06-08
  • Rev Recd Date: 2015-07-25
  • Publish Date: 2016-11-10
  • The identity vector (i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the so-called total variability space represented with a thin and tall rectangular matrix. This paper introduces a novel algorithm to improve the computational and memory requirements for the application. In our method, the series of symmetric matrices can be represented by diagonal expression, sharing the same dictionary, which to some extent is analogous to eigen decomposition, and we name this algorithm Eigen decomposition like factorization (EDLF). Similar algorithms are listed for comparison, in the same condition, our method shows no disadvantages in identification accuracy.
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