Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy

• URL: http://arxiv.org/abs/2210.07002v2
• Date: Fri, 14 Oct 2022 13:28:52 GMT
• Title: Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy
• Authors: Sarina Meyer, Pascal Tilli, Pavel Denisov, Florian Lux, Julia Koch, Ngoc Thang Vu
• Abstract summary: Speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. We propose to tackle this issue by generating speaker embeddings using a generative adversarial network with Wasserstein distance as cost function.
• Score: 22.84840887071428
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. One of the challenges in this context is to create non-existent voices that sound as natural as possible. In this work, we propose to tackle this issue by generating speaker embeddings using a generative adversarial network with Wasserstein distance as cost function. By incorporating these artificial embeddings into a speech-to-text-to-speech pipeline, we outperform previous approaches in terms of privacy and utility. According to standard objective metrics and human evaluation, our approach generates intelligible and content-preserving yet privacy-protecting versions of the original recordings.

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