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Speaker anonymization is an effective privacy protection solution designed to conceal the speaker's identity while preserving the linguistic content and para-linguistic information of the original speech.
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V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An ASR corpus based on public domain audio books,” in Proc. ICASSP , 2015, pp. 5206–5210
2015
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C. Veaux, J. Yamagishi, K. MacDonald et al. , “Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit,” 2016
2016
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C. Magarinos, P. Lopez-Otero, L. Docio-Fernandez, E. Rodriguez-Banga, D. Erro, and C. Garcia-Mateo, “Reversible speaker de-identification using pre-trained transformation functions,” Computer Speech & Language , vol. 46, pp. 36–52, 2017
2017
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J. Qian, H. Du, J. Hou, L. Chen, T. Jung, and X. Li, “Hidebehind: Enjoy voice input with voiceprint unclonability and anonymity,” in Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems , 2018, pp. 82–94
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A. Nautsch, C. Jasserand, E. Kindt, M. Todisco, I. Trancoso, and N. Evans, “The gdpr & speech data: Reflections of legal and technology communities, first steps towards a common understanding,” in Proc. INTERSPEECH , 2019, p. 3695–3699
2019
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H. Zen, V. Dang, R. Clark, Y. Zhang, R. J. Weiss, Y. Jia, Z. Chen, and Y. Wu, “Libritts: A corpus derived from librispeech for text-to-speech,” in Proc. INTERSPEECH , 2019, pp. 1526–1530
2019
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A. Razavi, A. van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with VQ-VAE-2,” in Proc. NeurIPS , 2019, pp. 14 837–14 847
2019
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. ICLR , 2019
2019
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R. K. Das, X. Tian, T. Kinnunen, and H. Li, “The attacker’s perspective on automatic speaker verification: An overview,” in Proc. INTERSPEECH , 2020, pp. 4213–4217
2020
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N. A. Tomashenko, B. M. L. Srivastava, X. Wang, E. Vincent, A. Nautsch, J. Yamagishi, N. W. D. Evans, J. Patino, J. Bonastre, P. Noé, and M. Todisco, “Introducing the voiceprivacy initiative,” in Proceedings INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association , 2020, pp. 1693–1697
2020
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P. Gupta, G. P. Prajapati, S. Singh, M. R. Kamble, and H. A. Patil, “Design of voice privacy system using linear prediction,” in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2020 . IEEE, 2020, pp. 543–549
2020
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S. P. Dubagunta, R. J. J. H. van Son, and M. Magimai.-Doss, “Adjustable deterministic pseudonymisation of speech: Idiap-nki’s submission to voiceprivacy 2020 challenge,” 2020. [Online]. Available: https://www.voiceprivacychallenge.org/docs/Idiap-NKI.pdf
2020
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C. O. Mawalim, K. Galajit, J. Karnjana, and M. Unoki, “X-vector singular value modification and statistical-based decomposition with ensemble regression modeling for speaker anonymization system,” in Proceedings INTERSPEECH 2020, 21st Annual Conference of the International Speech Communication Association , 2020, pp. 1703–1707
2020
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V. Pratap, Q. Xu, A. Sriram, G. Synnaeve, and R. Collobert, “MLS: A large-scale multilingual dataset for speech research,” in Proc. INTERSPEECH , 2020, pp. 2757–2761
2020
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2020
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B. Desplanques, J. Thienpondt, and K. Demuynck, “ECAPA-TDNN: emphasized channel attention, propagation and aggregation in TDNN based speaker verification,” in Proc. INTERSPEECH , 2020, pp. 3830–3834
2020
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P. Noé, J. Bonastre, D. Matrouf, N. A. Tomashenko, A. Nautsch, and N. W. D. Evans, “Speech pseudonymisation assessment using voice similarity matrices,” in Proc. INTERSPEECH , 2020, pp. 1718–1722
2020
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H. Turner, G. Lovisotto, and I. Martinovic, “Generating identities with mixture models for speaker anonymization,” Computer Speech Language , vol. 72, p. 101318, 2022
2022
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2022
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X. Miao, X. Wang, E. Cooper, J. Yamagishi, and N. A. Tomashenko, “Language-independent speaker anonymization approach using self-supervised pre-trained models,” in Proc. Odyssey 2022: The Speaker and Language Recognition Workshop , 2022, pp. 279–286. [Online]. Available: https://doi.org/10.21437/Odyssey.2022-39
2022
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A. Babu, C. Wang, A. Tjandra, K. Lakhotia, Q. Xu, N. Goyal, K. Singh, P. von Platen, Y. Saraf, J. Pino, A. Baevski, A. Conneau, and M. Auli, “XLS-R: self-supervised cross-lingual speech representation learning at scale,” in Proc. INTERSPEECH , 2022, pp. 2278–2282
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T. Zhou, Y. Zhao, and J. Wu, “Resnext and res2net structures for speaker verification,” in 2021 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2021, pp. 301–307
2021
Cited alongside, same era.
J. Patino, N. A. Tomashenko, M. Todisco, A. Nautsch, and N. W. D. Evans, “Speaker anonymisation using the mcadams coefficient,” in Proceedings INTERSPEECH 2021, 22nd Annual Conference of the International Speech Communication Association , 2021, pp. 1099–1103
2021
Cited alongside, same era.
C. Huang, Y. Y. Lin, H. Lee, and L. Lee, “Defending your voice: Adversarial attack on voice conversion,” in IEEE Spoken Language Technology Workshop, SLT , 2021, pp. 552–559
2021
Cited alongside, same era.
D. Min, D. B. Lee, E. Yang, and S. J. Hwang, “Meta-stylespeech: Multi-speaker adaptive text-to-speech generation,” in Proc. ICML , 2021, pp. 7748–7759
2021
Cited alongside, same era.
N. Tomashenko, X. Wang, E. Vincent, J. Patino, B. M. L. Srivastava, P.-G. Noé, A. Nautsch, N. Evans, J. Yamagishi, B. O’Brien et al. , “The voiceprivacy 2020 challenge: Results and findings,” Computer Speech & Language , vol. 74, p. 101362, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Tavi, T. Kinnunen, and R. G. Hautamäki, “Improving speaker de-identification with functional data analysis of f0 trajectories,” Speech Communication , vol. 140, pp. 1–10, 2022
2022
Cited alongside, same era.
C. O. Mawalim, S. Okada, and M. Unoki, “Speaker anonymization by pitch shifting based on time-scale modification,” in Proceedings of 2nd Symposium on Security and Privacy in Speech Communication , 2022, pp. 35–42
2022
Cited alongside, same era.
2022
Later among the works it cites.
P. Noé, A. Nautsch, N. W. D. Evans, J. Patino, J. Bonastre, N. A. Tomashenko, and D. Matrouf, “Towards a unified assessment framework of speech pseudonymisation,” Comput. Speech Lang. , vol. 72, p. 101299, 2022
2022
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T. Saeki, D. Xin, W. Nakata, T. Koriyama, S. Takamichi, and H. Saruwatari, “UTMOS: utokyo-sarulab system for voicemos challenge 2022,” in Proc. INTERSPEECH , 2022, pp. 4521–4525
2022
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2023
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J. Yao, Q. Wang, Y. Lei, P. Guo, L. Xie, N. Wang, and J. Liu, “Distinguishable speaker anonymization based on formant and fundamental frequency scaling,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
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S. Meyer, F. Lux, J. Koch, P. Denisov, P. Tilli, and N. T. Vu, “Prosody is not identity: A speaker anonymization approach using prosody cloning,” in Proc. ICASSP , 2023
2023
Later among the works it cites.
2023
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X. Miao, X. Wang, E. Cooper, J. Yamagishi, and N. Tomashenko, “Speaker anonymization using orthogonal householder neural network,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in Proc. ICML , 2023, pp. 28 492–28 518
2023
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2024
Closest in time.
M. Panariello, F. Nespoli, M. Todisco, and N. Evans, “Speaker anonymization using neural audio codec language models,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 4725–4729
2024
Closest in time.