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On the invertibility of a voice privacy system using embedding alignement

Abstract : This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques. We use Wasserstein-Procrustes (an algorithm initially designed for unsupervised translation) or Procrustes analysis to match two sets of x-vectors, before and after voice anonymization, to mimic this transformation as a rotation function. We compute the optimal rotation and compare the results of this approximation to the official Voice Privacy Challenge results. We show that a complex system like the baseline of the Voice Privacy Challenge can be approximated by a rotation, estimated using a limited set of x-vectors. This paper studies the space of solutions for voice anonymization within the specific scope of rotations. Rotations being reversible, the proposed method can recover up to 62% of the speaker identities from anonymized embeddings.
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Contributor : Thomas Thebaud Connect in order to contact the contributor
Submitted on : Friday, October 8, 2021 - 1:24:23 PM
Last modification on : Friday, July 15, 2022 - 12:22:20 PM


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  • HAL Id : hal-03356021, version 2
  • ARXIV : 2110.05431


Pierre Champion, Thomas Thebaud, Gaël Le Lan, Anthony Larcher, Denis Jouvet. On the invertibility of a voice privacy system using embedding alignement. ASRU 2021 - IEEE Automatic Speech Recognition and Understanding Workshop, Dec 2021, Cartagena, Colombia. ⟨hal-03356021v2⟩



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