Voice Conversion-based Privacy through Adversarial Information Hiding

Fuente: arXiv
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Auteurs principaux: Webber, Jacob J, Watts, Oliver, Henter, Gustav Eje, Williams, Jennifer, King, Simon
Format: Preprint
Publié: 2024
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author Webber, Jacob J
Watts, Oliver
Henter, Gustav Eje
Williams, Jennifer
King, Simon
author_facet Webber, Jacob J
Watts, Oliver
Henter, Gustav Eje
Williams, Jennifer
King, Simon
contents Privacy-preserving voice conversion aims to remove only the attributes of speech audio that convey identity information, keeping other speech characteristics intact. This paper presents a mechanism for privacy-preserving voice conversion that allows controlling the leakage of identity-bearing information using adversarial information hiding. This enables a deliberate trade-off between maintaining source-speech characteristics and modification of speaker identity. As such, the approach improves on voice-conversion techniques like CycleGAN and StarGAN, which were not designed for privacy, meaning that converted speech may leak personal information in unpredictable ways. Our approach is also more flexible than ASR-TTS voice conversion pipelines, which by design discard all prosodic information linked to textual content. Evaluations show that the proposed system successfully modifies perceived speaker identity whilst well maintaining source lexical content.
format Preprint
id arxiv_https___arxiv_org_abs_2409_14919
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Voice Conversion-based Privacy through Adversarial Information Hiding
Webber, Jacob J
Watts, Oliver
Henter, Gustav Eje
Williams, Jennifer
King, Simon
Sound
Audio and Speech Processing
Privacy-preserving voice conversion aims to remove only the attributes of speech audio that convey identity information, keeping other speech characteristics intact. This paper presents a mechanism for privacy-preserving voice conversion that allows controlling the leakage of identity-bearing information using adversarial information hiding. This enables a deliberate trade-off between maintaining source-speech characteristics and modification of speaker identity. As such, the approach improves on voice-conversion techniques like CycleGAN and StarGAN, which were not designed for privacy, meaning that converted speech may leak personal information in unpredictable ways. Our approach is also more flexible than ASR-TTS voice conversion pipelines, which by design discard all prosodic information linked to textual content. Evaluations show that the proposed system successfully modifies perceived speaker identity whilst well maintaining source lexical content.
title Voice Conversion-based Privacy through Adversarial Information Hiding
topic Sound
Audio and Speech Processing
url https://arxiv.org/abs/2409.14919