Abstract
In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected. In this context, an attacker with maximum knowledge about the anonymization system can not infer the target identity. This article proposed to constrain the target selection to a specific identity, i.e., removing the random selection of identity, to evaluate the extreme threat under a white-box assessment (the attacker has complete knowledge about the system). Targeting a unique identity also allows us to investigate whether some target’s identities are better than others to anonymize a given speaker.
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Acknowledgments
This work was supported in part by the French National Research Agency under project DEEP-PRIVACY (ANR-18-CE23-0018) and Région Grand Est. Experiments were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations.
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Champion, P., Jouvet, D., Larcher, A. (2021). Evaluating X-Vector-Based Speaker Anonymization Under White-Box Assessment. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_10
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DOI: https://doi.org/10.1007/978-3-030-87802-3_10
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