Are disentangled representations all you need to build speaker anonymization systems? - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Are disentangled representations all you need to build speaker anonymization systems?

Résumé

Speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns when speech data get collected. Speaker anonymization aims to transform a speech signal to remove the source speaker's identity while leaving the spoken content unchanged. Current methods perform the transformation by relying on content/speaker disentanglement and voice conversion. Usually, an acoustic model from an automatic speech recognition system extracts the content representation while an x-vector system extracts the speaker representation. Prior work has shown that the extracted features are not perfectly disentangled. This paper tackles how to improve features disentanglement, and thus the converted anonymized speech. We propose enhancing the disentanglement by removing speaker information from the acoustic model using vector quantization. Evaluation done using the VoicePrivacy 2022 toolkit showed that vector quantization helps conceal the original speaker identity while maintaining utility for speech recognition.
Fichier principal
Vignette du fichier
main.pdf (518.68 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03753746 , version 1 (19-08-2022)
hal-03753746 , version 2 (23-08-2022)
hal-03753746 , version 3 (06-12-2022)

Licence

Paternité

Identifiants

Citer

Pierre Champion, Denis Jouvet, Anthony Larcher. Are disentangled representations all you need to build speaker anonymization systems?. INTERSPEECH 2022 - Human and Humanizing Speech Technology, Sep 2022, incheon, South Korea. ⟨hal-03753746v3⟩
187 Consultations
245 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More