ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation

Paul-Gauthier Noé, Mohammad Mohammadamini, Driss Matrouf, Titouan Parcollet, Andreas Nautsch, Jean-François Bonastre

In speech technologies, speaker’s voice representation is used in many applications such as speech recognition, voice conversion, speech synthesis and, obviously, user authentication. Modern vocal representations of the speaker are based on neural embeddings. In addition to the targeted information, these representations usually contain sensitive information about the speaker, like the age, sex, physical state, education level or ethnicity. In order to allow the user to choose which information to protect, we introduce in this paper the concept of attribute-driven privacy preservation in speaker voice representation. It allows a person to hide one or more personal aspects to a potential malicious interceptor and to the application provider. As a first solution to this concept, we propose to use an adversarial autoencoding method that disentangles in the voice representation a given speaker attribute thus allowing its concealment. We focus here on the sex attribute for an Automatic Speaker Verification (ASV) task. Experiments carried out using the VoxCeleb datasets have shown that the proposed method enables the concealment of this attribute while preserving ASV ability.

doi: 10.21437/Interspeech.2021-1712

Cite as: Noé, P.-G., Mohammadamini, M., Matrouf, D., Parcollet, T., Nautsch, A., Bonastre, J.-F. (2021) Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation. Proc. Interspeech 2021, 1902-1906, doi: 10.21437/Interspeech.2021-1712

  author={Paul-Gauthier Noé and Mohammad Mohammadamini and Driss Matrouf and Titouan Parcollet and Andreas Nautsch and Jean-François Bonastre},
  title={{Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation}},
  booktitle={Proc. Interspeech 2021},