article

Linear prediction residual features for automatic speaker verification anti-spoofing

Author:

Cemal HanilçiAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 13

Pages 16099 - 16111

https://doi.org/10.1007/s11042-017-5181-0

Published: 01 July 2018 Publication History

Abstract

Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks. Anti-spoofing, determining whether a speech signal is natural/genuine or spoofed, is very important for improving the reliability of the ASV systems. Spoofing attacks using the speech signals generated using speech synthesis and voice conversion have recently received great interest due to the 2015 edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). In this paper, we propose to use linear prediction (LP) residual based features for anti-spoofing. Three different features extracted from LP residual signal were compared using the ASVspoof 2015 database. Experimental results indicate that LP residual phase cepstral coefficients (LPRPC) and LP residual Hilbert envelope cepstral coefficients (LPRHEC) obtained from the analytic signal of the LP residual yield promising results for anti-spoofing. The proposed features are found to outperform standard Mel-frequency cepstral coefficients (MFCC) and Cosine Phase (CosPhase) features. LPRPC and LPRHEC features give the smallest equal error rates (EER) for eight spoofing methods out of ten spoofing attacks in comparison to MFCC and CosPhase features.

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Cited By

Gao X(2022)A nonlinear prediction model for Chinese speech signal based on RBF neural networkMultimedia Tools and Applications10.1007/s11042-021-11612-681:4(5033-5049)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-021-11612-6
Garg SMankad S(2020)Voice liveness detection under feature fusion and cross-environment scenarioMultimedia Tools and Applications10.1007/s11042-020-09281-y79:37-38(26951-26967)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/s11042-020-09281-y
Singh MPati D(2019)Combining evidences from Hilbert envelope and residual phase for detecting replay attacksInternational Journal of Speech Technology10.1007/s10772-019-09604-x22:2(313-326)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10772-019-09604-x

Index Terms

Linear prediction residual features for automatic speaker verification anti-spoofing
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

Index terms have been assigned to the content through auto-classification.

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 13

July 2018

1477 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2018

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Cited By

Gao X(2022)A nonlinear prediction model for Chinese speech signal based on RBF neural networkMultimedia Tools and Applications10.1007/s11042-021-11612-681:4(5033-5049)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-021-11612-6
Garg SMankad S(2020)Voice liveness detection under feature fusion and cross-environment scenarioMultimedia Tools and Applications10.1007/s11042-020-09281-y79:37-38(26951-26967)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/s11042-020-09281-y
Singh MPati D(2019)Combining evidences from Hilbert envelope and residual phase for detecting replay attacksInternational Journal of Speech Technology10.1007/s10772-019-09604-x22:2(313-326)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10772-019-09604-x

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