Partial Least Squares Based Total Variability Space Modeling for I-Vector Speaker Verification

CHEN Chen; HAN Jiqing

doi:10.1049/cje.2018.06.001

Volume 27 Issue 6

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CHEN Chen and HAN Jiqing, “Partial Least Squares Based Total Variability Space Modeling for I-Vector Speaker Verification,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1229-1233, 2018, doi: 10.1049/cje.2018.06.001

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CHEN Chen and HAN Jiqing, “Partial Least Squares Based Total Variability Space Modeling for I-Vector Speaker Verification,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1229-1233, 2018, doi: 10.1049/cje.2018.06.001

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Partial Least Squares Based Total Variability Space Modeling for I-Vector Speaker Verification

doi: 10.1049/cje.2018.06.001

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61471145, No.91120303).

More Information

Corresponding author: HAN Jiqing (corresponding author) received the Ph.D. degree in computer science and technology from Harbin Institute of Technology. He is a professor and Ph.D. supervisor of School of Computer Science and Technology. He is a committee member of Automatic Discipline, National Natural Science Foundation of China, a committee member of National Science and Technology Awards of China, the vice Chairman of Society of Speech Processing, Association for Chinese Information Processing, etc. His main research fields are speech signal processing and audio information retrieval. He has won three Second-Prize and two Third-Prize awards of Science and Technology of Ministry/Province. He has published more than 200 papers and 4 books. He has obtained 9 national invention patents. (Email:jqhan@hit.edu.cn)
Received Date: 2016-07-13
Rev Recd Date: 2017-04-05
Publish Date: 2018-11-10

Abstract

Abstract

As an effective and low-dimension representation for speech utterances with different lengths, i-vector method has drawn considerable attentions in speaker verification. Training a Total variability space (TVS) is one of the key parts in the i-vector method. However, the traditional training method only explores the relationship between different mean supervectors, ignoring priori category information of speakers, which results in a lack of discrimination. In the proposed method, a discriminative TVS based on Partial least squares (PLS) is estimated, in which both the correlation of intra-class and the distinction of inter-class are fully utilized due to using speaker labels, and the proposed method can achieve a better performance.
- Speaker verification,
- I-vector,
- Total variability space,
- Partial least squares

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References(18)

References

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