International Conference on Speech and Computer

SPECOM 2015: Speech and Computer pp 480-486 | Cite as

Vulnerability of Voice Verification System with STC Anti-spoofing Detector to Different Methods of Spoofing Attacks

  • Vadim Shchemelinin
  • Alexandr Kozlov
  • Galina Lavrentyeva
  • Sergey Novoselov
  • Konstantin Simonchik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9319)

Abstract

This paper explores the robustness of a text-independent voice verification system against different methods of spoofing attacks based on speech synthesis and voice conversion techniques. Our experiments show that spoofing attacks based on the speech synthesis are most dangerous, but the use of standard TV-JFA approach based spoofing detection module can reduce the False Acceptance error rate of the whole speaker recognition system from 80 % to 1 %.

Keywords

Spoofing Anti-spoofing Speaker recognition TV SVM 

Notes

Acknowledgments

This work was partially financially supported by the Government of Russian Federation, Grant 074-U01.

References

  1. 1.
    Shchemelinin, V., Simonchik, K.: Examining vulnerability of voice verification systems to spoofing attacks by means of a TTS system. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 132–137. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  2. 2.
    Shchemelinin, V., Topchina, M., Simonchik, K.: Vulnerability of voice verification systems to spoofing attacks by TTS voices based on automatically labeled telephone speech. In: Ronzhin, A., Potapova, R., Delic, V. (eds.) SPECOM 2014. LNCS, vol. 8773, pp. 475–481. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Aleinik, S., Matveev, Y.N.: Detection of clipped fragments in speech signals. Int. J. Electr. Electron. Sci. Eng. 8(2), 74–80 (2014)Google Scholar
  4. 4.
    Kenny, P.: Bayesian speaker verification with heavy tailed priors. In: Proceedings of the Odyssey Speaker and Language Recognition Workshop (2010)Google Scholar
  5. 5.
    Simonchik, K., Pekhovsky T., Shulipa, A., Afanasyev, A.: Supervized mixture of PLDA models for cross-channel speaker verification. In: Proceedings of the 13th Annual Conference of the International Speech Communication Association, Interspeech 2012 (2012)Google Scholar
  6. 6.
    Matveev, Yu., Simonchik, K.: The speaker identification system for the NIST SRE 2010. In: Proceedings of the 20th International Conference on Computer Graphics and Vision, GraphiCon 2010, pp. 315–319 (2010)Google Scholar
  7. 7.
    Kozlov, A., Kudashev, O., Matveev, Y., Pekhovsky, T., Simonchik, K., Shulipa, A.: SVID speaker recognition system for NIST SRE 2012. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 278–285. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  8. 8.
    Wu, Z., et al.: ASVspoof 2015: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan, Training, 10(15), 3750 (2014). http://www.spoofingchallenge.org
  9. 9.
    Novoselov, S., Pekhovsky, T., Simonchik, K.: STC speaker recognition system for the NIST i-Vector challenge. In: Proceedings of Odyssey 2014 - The Speaker and Language Recognition Workshop (2014)Google Scholar
  10. 10.
    Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52, 12–40 (2010)CrossRefGoogle Scholar
  11. 11.
    Wu, Z., et al.: ASVspoof 2015: the First Automatic Speaker Verification Spoofing and Countermeasures Challenge (2015). http://www.spoofingchallenge.org/is2015_asvspoof.pdf
  12. 12.
    Dutoit, T., et al.: Towards a voice conversion system based on frame selection. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2007)Google Scholar
  13. 13.
    Wu, Z., et al.: Exemplarbased unit selection for voice conversion utilizing temporal information. In: Interspeech (2013)Google Scholar
  14. 14.
    Fukada, T., Tokuda, K., Kobayashi, T., Imai, S.: An adaptive algorithm for mel-cepstral analysis of speech. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (1992)Google Scholar
  15. 15.
    Yamagishi, J., et al.: Analysis of speaker adaptation algorithms for HMM-based speech synthesis and a constrained smaplr adaptation algorithm. IEEE Trans. Audio Speech Lang. Process. 17(1), 66–83 (2009)CrossRefGoogle Scholar
  16. 16.
    Festvox project. http://festvox.org/
  17. 17.
    Novoselov, S., et al.: STC Anti-spoofing Systems for the ASVspoof 2015 Challenge. http://ris.ifmo.ru/wp-content/uploads/2015/06/Technical_report_ASVspoof2015_STC.pdf
  18. 18.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vadim Shchemelinin
    • 1
    • 2
  • Alexandr Kozlov
    • 2
  • Galina Lavrentyeva
    • 2
  • Sergey Novoselov
    • 1
    • 2
  • Konstantin Simonchik
    • 1
    • 2
  1. 1.ITMO UniversitySt. PetersburgRussia
  2. 2.Speech Technology Center LimitedSt. PetersburgRussia

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