  In recent years, the need for privacy preservation when manipulating or
storing personal data, including speech , has become a major issue. In this
paper, we present a system addressing the speaker-level anonymization problem.
We propose and evaluate a two-stage anonymization pipeline exploiting a
state-of-the-art anonymization model described in the Voice Privacy Challenge
2022 in combination with a zero-shot voice conversion architecture able to
capture speaker characteristics from a few seconds of speech. We show this
architecture can lead to strong privacy preservation while preserving pitch
information. Finally, we propose a new compressed metric to evaluate
anonymization systems in privacy scenarios with different constraints on
privacy and utility.
