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

Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline