A comparative study of speech anonymization metrics

Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie; Bellet, Aurelien; Tommasi, Marc; Vincent, Emmanuel

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]

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 ...
Show more >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 :