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arXiv · 2110.05431

On the invertibility of a voice privacy system using embedding alignement

Pierre Champion·Thomas Thebaud·Gaël Le Lan·Anthony Larcher·Denis Jouvet
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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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Pierre Champion, Thomas Thebaud, Gaël Le Lan, Anthony Larcher, Denis Jouvet. 2021-10-08. On the invertibility of a voice privacy system using embedding alignement. https://arxiv.org/abs/2110.05431

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