Abstract
Growing interest in automatic speaker verification (ASV) systems has lead to significant quality improvement of spoofing attacks on them. Many research works confirm that despite the low equal error rate (EER) ASV systems are still vulnerable to spoofing attacks. In this work we overview different acoustic feature spaces and classifiers to determine reliable and robust countermeasures against spoofing attacks. We compared several spoofing detection systems, presented so far, on the development and evaluation datasets of the Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015. Experimental results presented in this paper demonstrate that the use of magnitude and phase information combination provides a substantial input into the efficiency of the spoofing detection systems. Also wavelet-based features show impressive results in terms of equal error rate. In our overview we compare spoofing performance for systems based on different classifiers. Comparison results demonstrate that the linear SVM classifier outperforms the conventional GMM approach. However, many researchers inspired by the great success of deep neural networks (DNN) approaches in the automatic speech recognition, applied DNN in the spoofing detection task and obtained quite low EER for known and unknown type of spoofing attacks.
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References
Villalba, E., Lleida, E.: Speaker verification performance degradation against spoofing and tampering attacks. In: Proceedings of the FALA Workshop, pp. 131–134 (2010)
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilc, C., Sahidullah, M., Sizov, A.: ASVspoof 2015: the First Automatic Speaker Verification Spoofing and Countermeasures Challenge (2015). http://www.spoofingchallenge.org/is2015_asvspoof.pdf
Patel, T.B., Patil, H.A.: Combining Evidences from Mel Cepstral, Cochlear Filter Cepstral and Instantaneous Frequency Features for Detection of Natural vs. Spoofed Speech, Interspeech (2015)
Novoselov, S., Kozlov, A., Lavrentyeva, G., Simonchik, K., Shchemelinin, V.: STC Anti-spoofing systems for the ASVspoof Challenge arXiv:1507.08074 (2015)
Chen, N., Qian, Y., Dinkel, H., Chen, B., Kai, Y.: Robust Deep Feature for Spoofing Detection - The SJTU System for ASVspoof Challenge, Interspeech (2015)
Xiao, X., Tian, X., Steven, D., Haihua, X., Chng, E.S., Li, H.: Spoofing Speech Detection Using High Dimensional Magnitude and Phase Features: the NTU Approach for ASVspoof Challenge, Interspeech (2015)
Alam, M.J., Kenny, P., Bhattacharya, G., Stafylakis, T.: Development of CRIM System for the Automatic Speaker Verification Spoofing and Countermeasures Challenge, Interspeech (2015)
Liu, Y., Tian, Y., He, L., Liu, J., Johnson, M.T.: Simultaneous Utilization of Spectral Magnitude and Phase Information to Extract Supervectors for Speaker Verification Anti-spoofing, Interspeech (2015)
Weng, S., Chen, S., Lei, Y., Xuewei, W., Cai, W., Liu, Z., Li, M.: The SYSU System for the Interspeech Automatic Speaker Verification Spoofing and Countermeasures Challenge arXiv:1507.06711 (2015)
Wang, L., Yoshida, Y., Kawakami, Y., Nakagawa, S.: Relative phaseinformation for detecting human speech and spoofed speech, Interspeech (2015)
Villalba, J., Miguel, A., Ortega, A., Lleida, E.: Spoofing Detection with DNN and One-class SVM for the ASVspoof Challenge, Interspeech (2015)
Sanchez, J., Saratxaga, I., Hernaez, I., Navas, E., Erro, D.: The AHOLAB RPS SSD Spoofing Challenge submission, Interspeech (2015)
LIBLINEAR: A library for Large Linear Classification. https://www.csie.ntu.edu.tw/cjlin/liblinear/
Kinnunen, T., Rajan, P.: A practical, self adaptive voice activity detector for speaker verification with noisy telephone and microphone data. In: Proceedings of ICASSP, pp. 7229–7233 (2013)
Marcel, S., Nixon, M.S., Li, S.Z.: Handbook of Biometric Anti-spoofing: Trusted Biometrics Under Spoofing Attacks. Springer, London (2014)
Wu, Z., Evans, N., Kinnunen, T., Yamagishid, J., Alegreb, F., Lia, H.: Spoofing and countermeasures for speaker verification: a survey. Speech Commun. 66, 130–153 (2015)
Krawczyk, M., Gerkmann, T.: Shift phase reconstruction in voiced speech for an improved single-channel speech enhancement. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 22(12), 1931–1940 (2014)
Mallat, S.: A Wavelett Tour of Signal Processing, 3rd edn. Academic Press, New York (2008)
D’Haro, L., Cordoba, R., Salamea, C., Echeverry, J.: Extended phone log-likelihood ratio features and acoustic-based i-vectors for languages recognition. In: Proceedings of ICASSP, pp. 5379–5383. IEEE (2014)
Novoselov, S., Pekhovsky, T., Simonchik, K.: STC speaker recognition system for the NIST i-vector challenge. In: Proceedings of Odyssey - The Speaker and Language Recognition Workshop (2014)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep beliefnets. Neural Comput. 18, 1527–1554 (2006)
Acknowledgements
This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.578.21.0126 (ID RFMEFI57815X0126).
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Lavrentyeva, G., Novoselov, S., Simonchik, K. (2017). Anti-spoofing Methods for Automatic Speaker Verification System. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_17
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