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Evaluating X-vector-based Speaker Anonymization under White-box Assessment

Pierre Champion 1 Denis Jouvet 1 Anthony Larcher 2
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
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 whitebox 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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https://hal.archives-ouvertes.fr/hal-03351943
Contributor : Pierre Champion Connect in order to contact the contributor
Submitted on : Wednesday, September 29, 2021 - 1:09:08 PM
Last modification on : Friday, November 5, 2021 - 3:24:05 AM

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  • HAL Id : hal-03351943, version 3
  • ARXIV : 2109.11946

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Pierre Champion, Denis Jouvet, Anthony Larcher. Evaluating X-vector-based Speaker Anonymization under White-box Assessment. 23rd International Conference on Speech and Computer - SPECOM 2021, Sep 2021, Saint Petersburg, Russia. ⟨hal-03351943v3⟩

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