Title :

Privacy and utility of x-vector based speaker anonymization

Author(s) :

Srivastava, Brij Mohan Lal [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Machine Learning in Information Networks [MAGNET]
Maouche, Mohamed [Auteur]
Machine Learning in Information Networks [MAGNET]
Sahidullah, Md [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Vincent, Emmanuel [Auteur]
Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Tommasi, Marc [Auteur]
Machine Learning in Information Networks [MAGNET]
Tomashenko, Natalia [Auteur]
Laboratoire Informatique d'Avignon [LIA]
Wang, Xin [Auteur]
National Institute of Informatics [NII]
Yamagishi, Junichi [Auteur]
National Institute of Informatics [NII]

Journal title :

IEEE/ACM Transactions on Audio, Speech and Language Processing

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2022-06-15

ISSN :

2329-9290

English keyword(s) :

privacy
linkability
utility
speech recognition
speaker identification
speaker anonymization

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

We study the scenario where individuals (speakers) contribute to the publication of an anonymized speech corpus. Data users then leverage this public corpus to perform downstream tasks (such as training automatic speech ...
Show more >We study the scenario where individuals (speakers) contribute to the publication of an anonymized speech corpus. Data users then leverage this public corpus to perform downstream tasks (such as training automatic speech recognition systems), while attackers may try to de-anonymize itbased on auxiliary knowledge they collect. Motivated by this scenario, speaker anonymization aims to conceal the speaker identity while preserving the quality and usefulness of speech data. In this paper, we study x-vector based speaker anonymization, the leading approach in the recent Voice Privacy Challenge, which converts an input utterance into that of a random pseudo-speaker. We show that the strength of the anonymization varies significantly depending on how the pseudo-speaker is selected. In particular, we investigate four design choices: the distance measure between speakers, the region of x-vector space where the pseudo-speaker is mapped, the gender selection and whether to use speaker or utterance level assignment. We assess the quality of anonymization from the perspective of the three actors involved in our threat model, namely the speaker, the user and the attacker. To measure privacy and utility, we use respectively the linkability score achieved by the attackers and the decoding word error rate incurred by an ASR model trained with the anonymized data. Experiments on LibriSpeech dataset confirm that the optimal combination ofdesign choices yield state-of-the-art performance in terms of privacy protection as well as utility. Experiments on Mozilla Common Voice dataset show that the best design choices with 50 speakers guarantee the same anonymization level against re-identification attack as raw speech with 20,000 speakers.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
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