  The use of modern vocoders in an analysis/synthesis pipeline allows us to
investigate high-quality voice conversion that can be used for privacy
purposes. Here, we propose to transform the speaker embedding and the pitch in
order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation
fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis
approach, which is consistent with the zero-evidence concept of privacy. This
approach significantly reduces the information in speech related to the
speaker's sex while preserving speech content and some consistency in the
resulting protected voices.
