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A comparative study of speech anonymization ...
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Document type :
Communication dans un congrès avec actes
Title :
A comparative study of speech anonymization metrics
Author(s) :
Maouche, Mohamed [Auteur]
Machine Learning in Information Networks [MAGNET]
Srivastava, Brij Mohan Lal [Auteur]
Machine Learning in Information Networks [MAGNET]
Vauquier, Nathalie [Auteur]
Machine Learning in Information Networks [MAGNET]
Bellet, Aurelien [Auteur] refId
Machine Learning in Information Networks [MAGNET]
Tommasi, Marc [Auteur]
Machine Learning in Information Networks [MAGNET]
Vincent, Emmanuel [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Conference title :
INTERSPEECH 2020
City :
Shanghai
Country :
Chine
Start date of the conference :
2020-10-25
English keyword(s) :
anonymization
voice conversion
speaker recog- nition
privacy metrics
HAL domain(s) :
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
Speech anonymization techniques have recently been proposed for preserving speakers' privacy. They aim at concealing speak-ers' identities while preserving the spoken content. In this study, we compare three metrics proposed ...
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Speech anonymization techniques have recently been proposed for preserving speakers' privacy. They aim at concealing speak-ers' identities while preserving the spoken content. In this study, we compare three metrics proposed in the literature to assess the level of privacy achieved. We exhibit through simulation the differences and blindspots of some metrics. In addition, we conduct experiments on real data and state-of-the-art anonymiza-tion techniques to study how they behave in a practical scenario. We show that the application-independent log-likelihood-ratio cost function C min llr provides a more robust evaluation of privacy than the equal error rate (EER), and that detection-based metrics provide different information from linkability metrics. Interestingly , the results on real data indicate that current anonymiza-tion design choices do not induce a regime where the differences between those metrics become apparent.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
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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