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On the invertibility of a voice privacy system using embedding alignement

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  • Additional Information
    • Contributors:
      Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH); Inria Nancy - Grand Est; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD); Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA); Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA); Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS); Laboratoire d'Informatique de l'Université du Mans (LIUM); Le Mans Université (UM); Orange Labs R&D Rennes; France Télécom; Orange Labs Lannion; This work was supported in part by the French National Research Agency under project DEEP-PRIVACY (ANR-18-CE23-0018) and RégionGrand 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.; Grid'5000; ANR-18-CE23-0018,DEEP-PRIVACY,Apprentissage distribué, personnalisé, préservant la privacité pour le traitement de la parole(2018)
    • Publication Information:
      HAL CCSD
    • Publication Date:
      2021
    • Collection:
      Université de Lorraine: HAL
    • Subject Terms:
      Cartagena; Colombia
    • Abstract:
      International audience ; 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.
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/2110.05431; hal-03356021; https://hal.science/hal-03356021; https://hal.science/hal-03356021v2/document; https://hal.science/hal-03356021v2/file/main.pdf; ARXIV: 2110.05431
    • Online Access:
      https://hal.science/hal-03356021
      https://hal.science/hal-03356021v2/document
      https://hal.science/hal-03356021v2/file/main.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.723E895D