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.

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