DiffAnon: Diffusion-based Prosody Control for Voice Anonymization

Abstract:To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing methods either discard prosody for privacy or lack a principled mechanism to control the utility-privacy trade-off, operating at fixed design points. We propose DiffAnon, a diffusion-based anonymization method with classifier-free guidance (CFG) that provides explicit, continuous inference-time control over prosody preservation. DiffAnon refines acoustic detail over semantic embeddings of an RVQ codec, enabling smooth interpolation between anonymization strength and prosodic fidelity within a single model. To the best of our knowledge, it is the first voice anonymization framework to provide structured, interpolatable inference-time prosody control. Experiments demonstrate structured trade-off behavior, achieving strong utility while maintaining competitive privacy across controllable operating points.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)

arXiv:2604.26281 [eess.AS]

(or arXiv:2604.26281v2 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2604.26281 Focus to learn more arXiv-issued DOI via DataCite

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