Fetching the paper…
Reading the bibliography…
Anonymisation has the goal of manipulating speech signals in order to degrade the reliability of automatic approaches to speaker recognition, while preserving other aspects of speech, such as those relating to intelligibility and naturalness.
S. McAdams, “Spectral fusion, spectral parsing and the formation of the auditory image,” Ph. D. Thesis, Stanford , 1984
1984
Earlier work this paper cites.
J. Cohen, T. Kamm, and A. G. Andreou, “Vocal tract normalization in speech recognition: Compensating for systematic speaker variability,” The Journal of the Acoustical Society of America , vol. 97, no. 5, pp. 3246–3247, 1995
1995
Earlier work this paper cites.
C. Dodge and T. A. Jerse, Computer Music: Synthesis, Composition and Performance , 2nd ed. Macmillan Library Reference, 1997
1997
Earlier work this paper cites.
L. Lee and R. Rose, “A frequency warping approach to speaker normalization,” IEEE Transactions on speech and audio processing , vol. 6, no. 1, pp. 49–60, 1998
1998
Earlier work this paper cites.
Q. Jin, A. R. Toth, T. Schultz, and A. W. Black, “Speaker de-identification via voice transformation,” in Proc. ASRU . IEEE, 2009, pp. 529–533
2009
Earlier work this paper cites.
M. Pobar and I. Ipšić, “Online speaker de-identification using voice transformation,” in 2014 37th International convention on information and communication technology, electronics and microelectronics (mipro) . IEEE, 2014, pp. 1264–1267
2014
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an ASR corpus based on public domain audio books,” in Proc. ICASSP . IEEE, 2015, pp. 5206–5210
2015
Earlier work this paper cites.
C. Veaux, J. Yamagishi, K. MacDonald et al. , “CSTR VCTK corpus: English multi-speaker corpus for CSTR Voice Cloning Toolkit,” 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Magariños, 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
Cited alongside, same era.
F. Fang, X. Wang, J. Yamagishi, I. Echizen, M. Todisco, N. Evans, and J.-F. Bonastre, “Speaker Anonymization Using X-vector and Neural Waveform Models,” in Proc. 10th ISCA Speech Synthesis Workshop , 2018, pp. 155–160
2018
Cited alongside, same era.
F. Bahmaninezhad, C. Zhang, and J. H. Hansen, “Convolutional neural network based speaker de-identification.” in Proc. Odyssey , 2018, pp. 255–260
2018
Cited alongside, same era.
J. Qian, H. Du, J. Hou, L. Chen, T. Jung, and X.-Y. Li, “Hidebehind: Enjoy Voice Input with Voiceprint Unclonability and Anonymity,” in Proc. ACM Conference on Embedded Networked Sensor Systems , 2018, pp. 82–94
2018
Cited alongside, same era.
——, “The VoicePrivacy 2020 Challenge evaluation plan,” 2020
2020
Closest in time.
P.-G. Noé, J.-F. Bonastre, D. Matrouf, N. Tomashenko, A. Nautsch, and N. Evans, “Speech Pseudonymisation Assessment Using Voice Similarity Matrices,” in Proc. INTERSPEECH , 2020
2020
Closest in time.
J. Patino, M. Todisco, A. Nautsch, and N. Evans, “Speaker anonymisation using the McAdams coefficient,” Eurecom, Tech. Rep. RR-20-343. 2020 [Online]. Available: http://www.eurecom.fr/publication/6190, Tech. Rep., 2020
2020
Closest in time.
P. Gupta, G. P. Prajapati, S. Singh, M. R. Kamble, and H. A. Patil, “Design of voice privacy system using linear prediction,” in Proc. APSIPA . IEEE, 2020, pp. 543–549
2020
Closest in time.
B. M. L. Srivastava, N. Vauquier, M. Sahidullah, A. Bellet, M. Tommasi, and E. Vincent, “Evaluating voice conversion-based privacy protection against informed attackers,” in Proc. ICASSP . IEEE, 2020, pp. 2802–2806
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
2019
Cited alongside, same era.
A. Nautsch, A. Jiménez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, A. Mtibaa et al. , “Preserving privacy in speaker and speech characterisation,” Computer Speech & Language , vol. 58, pp. 441–480, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
N. Tomashenko, B. M. L. Srivastava, X. Wang, E. Vincent, A. Nautsch, J. Yamagishi, N. Evans, J. Patino, J.-F. Bonastre, P.-G. Noé et al. , “Introducing the VoicePrivacy initiative,” in Proc. INTERSPEECH , 2020
2020
Cited alongside, same era.
2020
Closest in time.
B. M. L. Srivastava, N. Tomashenko, X. Wang, E. Vincent, J. Yamagishi, M. Maouche, A. Bellet, and M. Tommasi, “Design choices for x-vector based speaker anonymization,” in Proc. INTERSPEECH , 2020
2020
Closest in time.
A. Nautsch, J. Patino, N. Tomashenko, J. Yamagishi, P.-G. Noe, J.-F. Bonastre, M. Todisco, and N. Evans, “The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment,” in Proc. INTERSPEECH , 2020
2020
Closest in time.
H. Kai, S. Takamichi, S. Shiota, and H. Kiya, “Lightweight voice anonymization based on data-driven optimization of cascaded voice modification modules,” in Proc. IEEE SLT , 2021
2021
Closest in time.