Title :

The VoicePrivacy 2020 Challenge: Results and findings

Author(s) :

Tomashenko, Natalia [Auteur]
Laboratoire Informatique d'Avignon [LIA]
Wang, Xin [Auteur]
National Institute of Informatics [NII]
Vincent, Emmanuel [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Patino, Jose [Auteur]
Eurecom [Sophia Antipolis]
Srivastava, Brij Mohan Lal [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Machine Learning in Information Networks [MAGNET]
Noé, Paul-Gauthier [Auteur]
Laboratoire Informatique d'Avignon [LIA]
Nautsch, Andreas [Auteur]
Eurecom [Sophia Antipolis]
Evans, Nicholas [Auteur]
Eurecom [Sophia Antipolis]
Yamagishi, Junichi [Auteur]
University of Edinburgh [Edin.]
National Institute of Informatics [NII]
O'Brien, Benjamin [Auteur]
Laboratoire Parole et Langage [LPL]
Chanclu, Anaïs [Auteur]
Laboratoire Informatique d'Avignon [LIA]
Bonastre, Jean-François [Auteur]
Laboratoire Informatique d'Avignon [LIA]
Todisco, Massimiliano [Auteur]
Eurecom [Sophia Antipolis]
Maouche, Mohamed [Auteur]
Machine Learning in Information Networks [MAGNET]

Journal title :

Computer Speech and Language

Pages :

101362

Publisher :

Elsevier

Publication date :

2022-07

ISSN :

0885-2308

English keyword(s) :

automatic speech recognition
attack model
metrics
utility
privacy
anonymization
speech synthesis
voice conversion
speaker verification

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provide a systematic overview of the challenge ...
Show more >This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results. In particular, we describe the voice anonymization task and datasets used for system development and evaluation. Also, we present different attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and provide a summary description of the anonymization systems developed by the challenge participants. We report objective and subjective evaluation results for baseline and submitted systems. In addition, we present experimental results for alternative privacy metrics and attack models developed as a part of the post-evaluation analysis. Finally, we summarise our insights and observations that will influence the design of the next VoicePrivacy challenge edition and some directions for future voice anonymization research.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Apprentissage distribué, personnalisé, préservant la privacité pour le traitement de la parole
Clonage et protection de l'identité vocale
Open data, outils et challenges pour l'anonymisation des voix

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL