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M. Panariello

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Preprint Oct 2026

Preemptive defense against re-identification attacks on voice anonymization via adversarial perturbation

Voice anonymization (VA) is used to conceal the voice identities in speech data. Performance is usually estimated using automatic speaker verification (ASV) as a proxy to judge the capability of an attacker to re-identify speakers after anonymization. The defender anonymizes test utterances; the attacker compares them...

M. Panariello, Yi-Bo Bai, Massimiliano Todisco et al. · 0 citations
Preprint Sep 2026

Partial Accent-Control Editing in Frozen Speech Representations for Accent Conversion

Accent conversion is the task of modifying a speech recording so that it sounds closer to a target accent while preserving linguistic content and other speaker-related characteristics. Most accent conversion systems use trained, generative models. Although they can induce target-accented speech, the strength of accent...

Yang-Yang Qu, M. Panariello, Massimiliano Todisco et al. · 0 citations
Preprint Jul 2026

Positive-Incentive Noise Predictor for Adversarial Purification in Speaker Verification

The Positive-Incentive Noise Predictor (PnP) is proposed, the first framework that explicitly introduces positive-incentive noise ({\pi}-noise) into the purification task and effectively defends against adversarial attacks while preserving performance on natural speech.

Yibo Bai, Sizhou Chen, M. Panariello et al. · 0 citations

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