Speaker Recognition Using Discriminant Neighborhood Embedding

Chunyan LIANG; Wenhao YUAN; Yanling LI; Bin XIA; Wenzhu SUN

doi:10.11999/JEIT180761

Volume 41 Issue 7

Jul. 2019

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Chunyan LIANG, Wenhao YUAN, Yanling LI, Bin XIA, Wenzhu SUN. Speaker Recognition Using Discriminant Neighborhood Embedding[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1774-1778. doi: 10.11999/JEIT180761

Citation:

Chunyan LIANG, Wenhao YUAN, Yanling LI, Bin XIA, Wenzhu SUN. Speaker Recognition Using Discriminant Neighborhood Embedding[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1774-1778. doi: 10.11999/JEIT180761

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Speaker Recognition Using Discriminant Neighborhood Embedding

doi: 10.11999/JEIT180761

1.
College of Computer Science and Technology, Shandong University of Technology, Zibo 255049, China
2.
College of Computer and Information Engineering, Inner Mongolia Normal University, Hohhot 010022, China

Funds: The National Natural Science Foundation of China (11704229, 61701286, 61562068), The Shandong Provincial Natural Science Foundation (ZR2017LA011, ZR2015FL003, ZR2017MF047), The Project of Shandong Province Higher Educational Science and Technology Program (J17KA078), The Natural Science Foundation of Inner Mongolia Autonomous Region of China (2015MS0629)

Received Date: 2018-08-03
Rev Recd Date: 2019-01-21

Available Online: 2019-02-24

Publish Date: 2019-07-01

Abstract

Abstract

Discriminant Neighborhood Embedding (DNE) algorithm is introduced into the speaker recognition system. DNE is a manifold learning approach and aims at preserving the local neighborhood structure on the data manifold. As well, DNE has much more power in discrimination by sufficiently using the between-class discriminant information. The experimental results on the telephone-telephone core condition of the NIST 2010 Speaker Recognition Evaluation (SRE) dataset indicate the effectiveness of DNE algorithm.

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References(25)

References

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