@inproceedings{c7e941db205f485f9803b1a4aab47cdf,
title = "Quantifying Source Speaker Leakage in One-to-One Voice Conversion",
abstract = "Using a multi-accented corpus of parallel utterances for use with commercial speech devices, we present a case study to show that it is possible to quantify a degree of confidence about a source speaker's identity in the case of one-to-one voice conversion. Following voice conversion using a HiFi-GAN vocoder, we compare information leakage for a range speaker characteristics; assuming a 'worst-case' white-box scenario, we quantify our confidence to perform inference and narrow the pool of likely source speakers, reinforcing the regulatory obligation and moral duty that providers of synthetic voices have to ensure the privacy of their speakers' data.",
keywords = "evaluation, privacy, voice conversion",
author = "Scott Wellington and Xuechen Liu and Junichi Yamagishi",
year = "2024",
month = dec,
day = "11",
doi = "10.1109/BIOSIG61931.2024.10786731",
language = "English",
isbn = "9798350373721",
series = "BIOSIG 2024 - Proceedings of the 23rd International Conference of the Biometrics Special Interest Group",
publisher = "IEEE",
pages = "1--6",
editor = "Fadi Boutros and Naser Damer and Meiling Fang and Marta Gomez-Barrero and Kiran Raja and Christian Rathgeb and Sequeira, {Ana F.} and Massimiliano Todisco",
booktitle = "BIOSIG 2024 - Proceedings of the 23rd International Conference of the Biometrics Special Interest Group",
address = "USA United States",
note = "23rd International Conference of the Biometrics Special Interest Group, BIOSIG 2024 ; Conference date: 25-09-2024 Through 27-09-2024",
}