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Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion

Publication ,  Conference
Cai, W; Cai, D; Liu, W; Li, G; Li, M
Published in: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
January 1, 2017

The ongoing ASVspoof 2017 challenge aims to detect replay attacks for text dependent speaker verification. In this paper, we propose multiple replay spoofing countermeasure systems, with some of them boosting the CQCC-GMM baseline system after score level fusion. We investigate different steps in the system building pipeline, including data augmentation, feature representation, classification and fusion. First, in order to augment training data and simulate the unseen replay conditions, we converted the raw genuine training data into replay spoofing data with parametric sound reverberator and phase shifter. Second, we employed the original spectrogram rather than CQCC as input to explore the end-to-end feature representation learning methods. The spectrogram is randomly cropped into fixed size segments, and then fed into a deep residual netowrk (ResNet). Third, upon the CQCC features, we replaced the subsequent GMM classifier with deep neural networks including fully-connected deep neural network (FDNN) and Bidirectional Long Short Term Memory neural network (BLSTM). Experiments showed that data augmentation strategy can significantly improve the system performance. The final fused system achieves to 16.39 % EER on the test set of ASVspoof 2017 for the common task.

Duke Scholars

Published In

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

DOI

EISSN

1990-9772

ISSN

2308-457X

Publication Date

January 1, 2017

Volume

2017-August

Start / End Page

17 / 21
 

Citation

APA
Chicago
ICMJE
MLA
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Cai, W., Cai, D., Liu, W., Li, G., & Li, M. (2017). Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (Vol. 2017-August, pp. 17–21). https://doi.org/10.21437/Interspeech.2017-906
Cai, W., D. Cai, W. Liu, G. Li, and M. Li. “Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion.” In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2017-August:17–21, 2017. https://doi.org/10.21437/Interspeech.2017-906.
Cai W, Cai D, Liu W, Li G, Li M. Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2017. p. 17–21.
Cai, W., et al. “Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion.” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2017-August, 2017, pp. 17–21. Scopus, doi:10.21437/Interspeech.2017-906.
Cai W, Cai D, Liu W, Li G, Li M. Countermeasures for automatic speaker verification replay spoofing attack : on data augmentation, feature representation, classification and fusion. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2017. p. 17–21.

Published In

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

DOI

EISSN

1990-9772

ISSN

2308-457X

Publication Date

January 1, 2017

Volume

2017-August

Start / End Page

17 / 21