Title :

Supplementary material to the paper 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
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]

English keyword(s) :

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

HAL domain(s) :

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

English abstract : [en]

The VoicePrivacy 2020 Challenge focuses on developing anonymization solutions for speech technology. This report complements the summary results and analyses presented by Tomashenko et al. (2021). After quickly recalling ...
Show more >The VoicePrivacy 2020 Challenge focuses on developing anonymization solutions for speech technology. This report complements the summary results and analyses presented by Tomashenko et al. (2021). After quickly recalling the challenge design and the submitted anonymization systems, we provide more detailed results and analyses. First, we present objective evaluation results for the primary challenge metrics and for alternative metrics and attack models, and we compare them with each other. Second, we present subjective evaluation results for speaker verifiability, speech naturalness, and speech intelligibility. Finally, we compare these objective and subjective evaluation results with each other.Show less >

Language :

Anglais

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

Comment :

Supplementary material to the paper "The VoicePrivacy 2020 Challenge: Results and findings" (https://hal.archives-ouvertes.fr/hal-03332224) submitted to CSL.

Collections :

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

Source :

Harvested from HAL

Submission date :

2021-11-24T02:01:38Z