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A great deal of recent research effort on speech spoofing countermeasures has been invested into back-end neural networks and training criteria.
S. Holm, “A simple sequentially rejective multiple test procedure,” Scandinavian journal of statistics , pp. 65–70, 1979
1979
Earlier work this paper cites.
S. Bengio and J. Mariéthoz, “A statistical significance test for person authentication,” in Proc. Odyssey , 2004
2004
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. ICLR , 2014
2014
Earlier work this paper cites.
H. Dinkel, N. Chen, Y. Qian, and K. Yu, “End-to-end spoofing detection with raw waveform CLDNNS,” in Proc. ICASSP . IEEE, 2017, pp. 4860–4864
2017
Earlier work this paper cites.
C. Zhang, C. Yu, and J. H. L. Hansen, “An investigation of deep-learning frameworks for speaker verification antispoofing,” IEEE Journal of Selected Topics in Signal Processing , vol. 11, no. 4, pp. 684–694, 2017
2017
Earlier work this paper cites.
K. R. Alluri, S. Achanta, S. R. Kadiri, S. V. Gangashetty, and A. K. Vuppala, “SFF Anti-Spoofer: IIIT-H Submission for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017,” in Proc. Interspeech 2017 , 2017, pp. 107–111
2017
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in Proc. CVPR , 2017, pp. 212–220
2017
Earlier work this paper cites.
X. Wu, R. He, Z. Sun, and T. Tan, “A light cnn for deep face representation with noisy labels,” IEEE Transactions on Information Forensics and Security , vol. 13, no. 11, pp. 2884–2896, 2018
2018
Earlier work this paper cites.
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” in Proc. CVPR , 2018, pp. 5265–5274
2018
Earlier work this paper cites.
F. Wang, J. Cheng, W. Liu, and H. Liu, “Additive margin softmax for face verification,” IEEE Signal Processing Letters , vol. 25, no. 7, pp. 926–930, 2018
2018
Earlier work this paper cites.
Y. Zhu, T. Ko, D. Snyder, B. Mak, and D. Povey, “Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification,” in Proc. Interspeech 2018 , 2018, pp. 3573–3577
2018
Earlier work this paper cites.
H. Dinkel, Y. Qian, and K. Yu, “Investigating raw wave deep neural networks for end-to-end speaker spoofing detection,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 26, no. 11, pp. 2002–2014, 2018
2018
Earlier work this paper cites.
T. Kinnunen, K. A. Lee, H. Delgado, N. Evans, M. Todisco, M. Sahidullah, J. Yamagishi, and D. A. Reynolds, “t-DCF: a detection cost function for the tandem assessment of spoofing countermeasures and automatic speaker verification,” in Proc. Odyssey , 2018, pp. 312–319
2018
Cited alongside, same era.
G. Lavrentyeva, S. Novoselov, A. Tseren, M. Volkova, A. Gorlanov, and A. Kozlov, “STC Antispoofing Systems for the ASVspoof2019 Challenge,” in Proc. Interspeech , 2019, pp. 1033–1037
2019
Cited alongside, same era.
X. Zhang, R. Zhao, J. Yan, M. Gao, Y. Qiao, X. Wang, and H. Li, “P2sgrad: Refined gradients for optimizing deep face models,” in Proc. CVPR , 2019, pp. 9906–9914
2019
Cited alongside, same era.
B. Chettri, D. Stoller, V. Morfi, M. A. M. Ramírez, E. Benetos, and B. L. Sturm, “Ensemble Models for Spoofing Detection in Automatic Speaker Verification,” in Proc. Interspeech , 2019, pp. 1018–1022
2019
Cited alongside, same era.
H. Tak, J. Patino, M. Todisco, A. Nautsch, N. Evans, and A. Larcher, “End-to-end anti-spoofing with RawNet2,” Proc. ICASSP , pp. 6369—-6373, 2020
2020
Later among the works it cites.
H. Tak, J. Patino, A. Nautsch, N. Evans, and M. Todisco, “An Explainability Study of the Constant Q Cepstral Coefficient Spoofing Countermeasure for Automatic Speaker Verification,” in Proc. Odyssey , 2020, pp. 333–340
2020
Later among the works it cites.
B. Chettri, T. Kinnunen, and E. Benetos, “Subband Modeling for Spoofing Detection in Automatic Speaker Verification,” in Proc. Odyssey , 2020, pp. 341–348
2020
Later among the works it cites.
H. Tak, J. Patino, A. Nautsch, N. Evans, and M. Todisco, “Spoofing Attack Detection Using the Non-Linear Fusion of Sub-Band Classifiers,” in Proc. Interspeech , 2020, pp. 1106–1110
2020
Later among the works it cites.
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C.-I. Lai, A. Abad, K. Richmond, J. Yamagishi, N. Dehak, and S. King, “Attentive filtering networks for audio replay attack detection,” in Proc. ICASSP . IEEE, 2019, pp. 6316–6320
2019
Cited alongside, same era.
C.-I. Lai, N. Chen, J. Villalba, and N. Dehak, “ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual Networks,” in Proc. Interspeech , 2019, pp. 1013–1017
2019
Cited alongside, same era.
W. Cai, H. Wu, D. Cai, and M. Li, “The DKU Replay Detection System for the ASVspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, and Fusion,” in Proc. Interspeech , 2019, pp. 1023–1027
2019
Cited alongside, same era.
Y. Yang, H. Wang, H. Dinkel, Z. Chen, S. Wang, Y. Qian, and K. Yu, “The SJTU Robust Anti-Spoofing System for the ASVspoof 2019 Challenge,” in Proc. Interspeech 2019 , 2019, pp. 1038–1042
2019
Cited alongside, same era.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proc. CVPR , 2019, pp. 4690–4699
2019
Cited alongside, same era.
Y. Liu, L. He, and J. Liu, “Large Margin Softmax Loss for Speaker Verification,” in Proc. Interspeech 2019 , 2019, pp. 2873–2877
2019
Cited alongside, same era.
M. Todisco, X. Wang, V. Vestman, M. Sahidullah, H. Delgado, A. Nautsch, J. Yamagishi, N. Evans, T. H. Kinnunen, and K. A. Lee, “ASVspoof 2019: future horizons in spoofed and fake audio detection,” in Proc. Interspeech , 2019, pp. 1008–1012
2019
Cited alongside, same era.
T. Chen, A. Kumar, P. Nagarsheth, G. Sivaraman, and E. Khoury, “Generalization of Audio Deepfake Detection,” in Proc. Odyssey , 2020, pp. 132–137
2020
Cited alongside, same era.
Z. Wu, R. K. Das, J. Yang, and H. Li, “Light Convolutional Neural Network with Feature Genuinization for Detection of Synthetic Speech Attacks,” in Proc. Interspeech 2020 , 2020, pp. 1101–1105
2020
Later among the works it cites.
B. Chettri, E. Benetos, and B. L. T. Sturm, “Dataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmark,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 3018–3028, 2020
2020
Later among the works it cites.
X. Wang, J. Yamagishi, M. Todisco, H. Delgado, A. Nautsch, N. Evans, M. Sahidullah, V. Vestman, T. Kinnunen, K. A. Lee, L. Juvela, P. Alku, Y.-H. Peng, H.-T. Hwang, Y. Tsao, H.-M. Wang, S. L. Maguer, M. Becker, F. Henderson, R. Clark, Y. Zhang, Q. Wang, Y. Jia, K. Onuma, K. Mushika, T. Kaneda, Y. Jiang, L.-J. Liu, Y.-C. Wu, W.-C. Huang, T. Toda, K. Tanaka, H. Kameoka, I. Steiner, D. Matrouf, J.-F. Bonastre, A. Govender, S. Ronanki, J.-X. Zhang, and Z.-H. Ling, “ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech,” Computer Speech & Language , vol. 64, p. 101114, nov 2020
2020
Later among the works it cites.
A. Nautsch, X. Wang, N. Evans, T. Kinnunen, V. Vestman, M. Todisco, H. Delgado, M. Sahidullah, J. Yamagishi, and K. A. Lee, “ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech,” IEEE Transactions on Biometrics, Behavior, and Identity Science , 2021
2021
Closest in time.
Y. Zhang, F. Jiang, and Z. Duan, “One-Class Learning Towards Synthetic Voice Spoofing Detection,” IEEE Signal Processing Letters , vol. 28, pp. 937–941, 2021
2021
Closest in time.
A. Luo, E. Li, Y. Liu, X. Kang, and Z. J. Wang, “A Capsule Network Based Approach for Detection of Audio Spoofing Attacks,” in Proc. ICASSP . IEEE, 2021, pp. 6359–6363
2021
Closest in time.
X. Li, N. Li, C. Weng, X. Liu, D. Su, D. Yu, and H. Meng, “Replay and synthetic speech detection with res2net architecture,” in Proc. ICASSP . IEEE, 2021, pp. 6354–6358
2021
Closest in time.