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Playback detection using machine learning with spectrogram features approach

Abstrakt

This paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect of speakers or playback devices: for instance with different speakers in training and test subsets. The playback detection systems were trained and tested on two speech datasets S1 and S2 manufactured independently by two different institutions. The test error for both datasets oscillates about the level of 1% for HOG+SVM and even below it for CNN in bigger S1 base. In cross validation scenario in which one base was used for training and second base for the test the results were very poor what suggests that the information relevant for playback detection appeared in each base in different way.

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Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2017 IEEE)

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Kategoria:
Aktywność konferencyjna
Typ:
materiały konferencyjne indeksowane w Web of Science
Tytuł wydania:
2017 10th International Conference on Human System Interactions (HSI) strony 31 - 35
Język:
angielski
Rok wydania:
2017
Opis bibliograficzny:
Dembski J., Rumiński J..: Playback detection using machine learning with spectrogram features approach, W: 2017 10th International Conference on Human System Interactions (HSI), 2017, ,.
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/hsi.2017.8004991
Bibliografia: test
  1. Z. Wu, S. Gao, E.S. Cling and H. Li, "A study on replay attack and anti-spoofing for text-dependent speaker verification", Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), 2014. otwiera się w nowej karcie
  2. W. Shang and M. Stevenson, "A playback attack detector for speaker verification systems", Communications, Control and Signal Processing ISCCSP 2008, 3rd International Symposium on, March 2008, pp. 11441149, 2008.
  3. J. Gałka, M, Grzywacz and R. Samborski, "Playback attack detec- tion for text-dependent speaker verification over telephone channels", Speech Communication, Volume 67, pp. 143-153, 2015. otwiera się w nowej karcie
  4. Z.F. Wang, G. Wei and Q.H. He, "Channel pattern noise based play- back attack detection algorithm for speaker recognition", Proceed- ings International Conference on Machine Learning and Cybernetics (ICMLC 2011), Vol. 4, IEEE, Guilin, China, pp. 17081713, 2011. otwiera się w nowej karcie
  5. J. Villalba and E. Lleida, "Preventing replay attacks on speaker veri- fication systems", Proceedings IEEE International Carnahan Confer- ence on Security Technology (ICCST 2011), IEEE, Barcelona, Spain, 2011. otwiera się w nowej karcie
  6. S. Shiota, F. Villavicencio, J. Yamagishi, N. Ono, I. Echizen and T. Matsui, "Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification", Proceed- ings Interspeech, ISCA, Dresden, Germany, pp. 239243, 2015. otwiera się w nowej karcie
  7. D. Luo, H. Wu and J. Huang, "Audio recapture detection using deep learning", Proceedings IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP 2015), IEEE, Chengdu, China, pp. 478482, 2015. otwiera się w nowej karcie
  8. M. Smiatacz, "Playback attack detection: the search for the ultimate set of antispoof features", Advances in Intelligent Systems and Computing, 2017, accepted for printing. otwiera się w nowej karcie
  9. M. Jones and P. Viola, "Face recognition using boosted local features", Technical Report MERL-TR-2003-25, Mitsubishi Electric Research Laboratory, 2003.
  10. D. Lowe, "Object recognition from local scale-invariant features", Proceedings of International Conference on Computer Vision, 1999. otwiera się w nowej karcie
  11. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 886-893, 2005. otwiera się w nowej karcie
  12. C. Cortes,V. Vapnik, "Support-vector networks" Machine Learning 20 (3), 273-297, 1995. otwiera się w nowej karcie
  13. C. Chang and C. Lin, "LIBSVM : a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. otwiera się w nowej karcie
  14. R.E. Schapire and Y. Freund, "Boosting the Margin: A New Explana- tion for the Effectiveness of Voting Methods", The Annals of Statistics, v. 26(5), 1651-1686, 1998. otwiera się w nowej karcie
  15. M. Jones and P. Viola, "Robust Real-Time Face Detection", M. Jones, International Journal of Computer Vision, 57(2), pp. 137-154, 2004.
Weryfikacja:
Politechnika Gdańska

wyświetlono 125 razy

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