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.
@article{arxiv.2306.16069,
title = {Two-Stage Voice Anonymization for Enhanced Privacy},
author = {Francesco Nespoli and Daniel Barreda and Joerg Bitzer and Patrick A. Naylor},
journal= {arXiv preprint arXiv:2306.16069},
year = {2023}
}