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Recently, adapting the idea of self-supervised learning (SSL) on continuous speech has started gaining attention.
C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in International Conference on Machine Learning . PMLR, 2021, pp. 1964–1974
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R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 3–18
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J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Membership inference attacks against generative models,” in Proceedings on Privacy Enhancing Technologies (PoPETs) , vol. 2019, no. 1. De Gruyter, 2019, pp. 133–152
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C. Song and V. Shmatikov, “Auditing data provenance in text-generation models,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 196–206
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J. Yamagishi, C. Veaux, and K. MacDonald, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92),” 2019
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L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 691–706
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C. Song and A. Raghunathan, “Information leakage in embedding models,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security , 2020, pp. 377–390
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D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in Proceedings of the 2020 ACM SIGSAC conference on computer and communications security , 2020, pp. 343–362
2020
Cited alongside, same era.
A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. T. Liu, S.-w. Yang, P.-H. Chi, P.-c. Hsu, and H.-y. Lee, “Mockingjay: Unsupervised speech representation learning with deep bidirectional transformer encoders,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6419–6423
2020
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M. Ravanelli, J. Zhong, S. Pascual, P. Swietojanski, J. Monteiro, J. Trmal, and Y. Bengio, “Multi-task self-supervised learning for robust speech recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6989–6993
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Cited alongside, same era.
2021
Closest in time.
M. A. Shah, J. Szurley, M. Mueller, A. Mouchtaris, and J. Droppo, “Evaluating the vulnerability of end-to-end automatic speech recognition models to membership inference attacks,” Proc. Interspeech 2021 , pp. 891–895, 2021
2021
Closest in time.
2021
Closest in time.
S. wen Yang, P.-H. Chi, Y.-S. Chuang, C.-I. J. Lai, K. Lakhotia, Y. Y. Lin, A. T. Liu, J. Shi, X. Chang, G.-T. Lin, T.-H. Huang, W.-C. Tseng, K. tik Lee, D.-R. Liu, Z. Huang, S. Dong, S.-W. Li, S. Watanabe, A. Mohamed, and H. yi Lee, “SUPERB: Speech Processing Universal PERformance Benchmark,” in Proc. Interspeech 2021 , 2021, pp. 1194–1198
2021
Closest in time.
R. Shokri, M. Strobel, and Y. Zick, “On the privacy risks of model explanations,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , 2021, pp. 231–241
2021
Closest in time.