International Conference on Speech and Computer

SPECOM 2015: Speech and Computer pp 137-143 | Cite as

Automatically Trained TTS for Effective Attacks to Anti-spoofing System

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

Abstract

This article is the proceeding of the priority research direction of the voice biometrics systems spoofing problem. We continue exploring speech synthesis spoofing attacks based on creating a text-to-speech voice. In our work we focused on the completely automatic way to create new voices for text-to-speech system and the investigation of the state-of-art spoofing detection system vulnerability to this spoofing attacks. Results obtained during our experiments demonstrate that 10 seconds of speech material is enough for EER increasement up to 19.67 %. Considering the fact, that automatic method for synthesis voiced training allows perpetrators to increase the amount of spoofing attacks to biometric systems, we raise the issue of relevance of a new type of spoofing attack, and development of the effective methods to detect it.

Keywords

Spoofing Anti-spoofing Speaker recognition TTS 

Notes

Acknowledgments

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

References

  1. 1.
    Wu, Z., et al.: Spoofing and countermeasures for speaker verification: a survey. Speech Commun. 66, 130–153 (2015)CrossRefGoogle Scholar
  2. 2.
    Matveev, Y.N.: Biometric technologies of person identification by voice and other modalities. Vestnik MGTU. Priborostroenie 3(3), 46–61 (2012)Google Scholar
  3. 3.
    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
  4. 4.
    Novoselov, S., Pekhovsky, T., Simonchik, K.: STC speaker recognition system for the NIST i-vector challenge. In: Proceedings of the Odyssey 2014 - The Speaker and Language Recognition Workshop (2014)Google Scholar
  5. 5.
    Villalba, E., Lleida, E.: Speaker verification performance degradation against spoofing and tampering attacks. In: Proceedings of the FALA 2010 Workshop, pp. 131–134 (2010)Google Scholar
  6. 6.
    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). (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Google Scholar
  7. 7.
    Marcel, S., Nixon, M.S., Li, S.Z.: Handbook of Biometric Anti-spoofing: Trusted Biometrics Under Spoofing Attacks. Springer, New York (2014)CrossRefGoogle Scholar
  8. 8.
    Wu, Z., et al.: ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge 2015. http://www.spoofingchallenge.org/is2015_asvspoof.pdf
  9. 9.
    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
  10. 10.
    Dehak, N., et al. : Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification. In: Proceedings of the Interspeech, pp. 1559–1562 (2009)Google Scholar
  11. 11.
    Chistikov, P., Korolkov, E.: Data-driven speech parameter generation for russian text-to-speech system. computational linguistics and intellectual technologies. In: Proceedings of the Annual International Conference “Dialogue”, Issue 11(18), vol. 1. pp. 103–111 (2012)Google Scholar
  12. 12.
    Simonchik, K., Shchemelinn, V.: “STC SPOOFING” database for text-dependent speaker recognition evaluation. In: Proceedings of SLTU-2014 Workshop St. Petersburg, Russia, pp. 221–224 (2014)Google Scholar
  13. 13.
    Tomashenko, N.A., Khokhlov, Y.Y.: Fast algorithm for automatic alignment of speech and imperfect text data. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 146–153. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  14. 14.
    Solomennik, A., Chistikov, P., Rybin, S., Talanov, A., Tomashenko, N.: Automation of new voice creation procedure for a russian TTS system. Vestnik MGTU. Priborostroenie 2, 29–32 (2013)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.
    Wu, Z., et al.: A study on replay attack and anti-spoofing for text-dependent speaker verification. In: Proceedings of the Asia-Pacific Signal Information Processing Association Annual Summit and Conference (APSIPA ASC) (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

Personalised recommendations