Speaker Recognition Based on Fisher Discrimination Dictionary Learning

WANG Wei; HAN Jiqing; ZHENG Tieran; ZHENG Guibin; TAO Yao

doi:10.11999/JEIT 150566

Volume 38 Issue 2

Feb. 2016

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WANG Wei, HAN Jiqing, ZHENG Tieran, ZHENG Guibin, TAO Yao. Speaker Recognition Based on Fisher Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566

Citation:

WANG Wei, HAN Jiqing, ZHENG Tieran, ZHENG Guibin, TAO Yao. Speaker Recognition Based on Fisher Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(2): 367-372. doi: 10.11999/JEIT 150566

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Speaker Recognition Based on Fisher Discrimination Dictionary Learning

doi: 10.11999/JEIT 150566

Funds:

The National Natural Science Foundation of China (61071181, 61471145), The Major Research Plan of the National Natural Science Foundation of China (91120303)

Received Date: 2015-05-13
Rev Recd Date: 2015-09-06
Publish Date: 2016-02-19

Abstract

Abstract

Motivated by the success of sparse representation in speaker recognition,?a good?dictionary?plays an important role in?sparse representation. In this paper, the structured dictionary learning is introduced to speaker recognition based on the Fisher criterion. In the process of learning the discrimination dictionary, each sub-dictionary of the learned dictionary corresponds to a class label, so the reconstruction error of the same training samples is small. Meanwhile, the sparse coding coefficients have small with-class scatter and big between-class scatter. On the NIST SRE 2003 database, the experimental results indicate that the proposed method achieves an Equal Error Rate (EER) of 7.62%, and the i-vector system based on cosine distance scoring gives an EER of 6.7%. Moreover, an EER of 5.07% is obtained by combining two systems.
- Speaker recognition,
- Dictionary learning,
- Sparse representation,
- Fisher Discrimination (FD)

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References(34)

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