arXiv subjects

Michele Panariello

Publications and source records attributed to Michele Panariello.

At least 19 recordsLinked to original sources

arXiv2026

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 modification is not controllable at inference time, making it difficult to analyse how the strength of accent conversion affects source preservation. We propose Partial Accent-Control Editing (PACE), an accent conversion framework based upon the editing of frozen WavLM representations without the training of an accent-conditioned generator. A constrained edit is first applied to source WavLM features, which are then fused with target-accent reference features retrieved from non-parallel accent examples. Fusion weights are used to control the trade-off between accent conversion strength and the degradation of other source attributes. Using a suite of five metrics, and with the cost of accent-entangled speaker similarity, we show that PACE is substantially superior to a pair of competitive baselines in terms of both accent conversion and source preservation.

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arXiv2026

Why Do You Say It Like That? A Phoneme-Level Framework for Explainable Speech Deepfake Detection

As the accuracy of speech deepfake detection improves with the use of self-supervised representations such as wav2vec 2.0 and HuBERT, understanding why the speech is classified as bona fide or deepfake remains an open challenge. In pursuit of more trustworthy and interpretable artificial intelligence, we introduce a phoneme-level analysis framework that connects model predictions to measurable phonetic units. Our post-hoc explainability method is generally applicable to a variety of speech deepfake detection systems based on convolutional neural networks since it leverages Gradient-weighted Class Activation Mapping in conjunction with speech recognition to generate saliency maps aligned with phonemes and pauses. This pipeline reveals statistically significant attack- and speaker-dependent phonetic cues associated with spoofed speech in terms that humans can understand. Experiments using ASVspoof 5 show comparable detection performance to similar architectures while providing linguistic interpretations across speakers and spoofing conditions.

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arXiv2026

Positive-Incentive Noise Predictor for Adversarial Purification in Speaker Verification

Modern automatic speaker verification (ASV) systems are vulnerable to adversarial perturbations. Diffusion-based purification has recently shown strong effectiveness against such perturbations, but its reverse denoising process requires iterative sampling and leads to high inference latency. We find that the forward noising process provides most of the robustness gain. Motivated by this observation, we reformulate adversarial purification as a learnable noising problem, and propose the Positive-Incentive Noise Predictor (PnP), the first framework that explicitly introduces positive-incentive noise (π-noise) into the purification task. PnP learns input-adaptive π-noise and mixes it with the input to improve the robustness of downstream ASV systems. Experiments on four advanced ASV backbones show that PnP effectively defends against adversarial attacks while preserving performance on natural speech. Compared with representative purification baselines, the proposed framework provides a competitive balance among defense effectiveness, impact on genuine utterances, and inference efficiency under white-box, black-box, and defender-aware adaptive attacks, with a real-time factor as low as 0.014. Moreover, PnP can be cascaded with a diffusion denoiser to further improve the perceptual quality of purified utterances. Code and purified audio examples are available at https://eurecom-asp.github.io/pnp/

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arXiv2026

Evaluating voice anonymisation using similarity rank disclosure

The evaluation of voice anonymisation remains challenging. Current practice relies on automatic speaker verification metrics such as the equal error rate (EER). Performance estimates dependent on the classifier and operating point provide an incomplete or even misleading characterisation of privacy risk. We investigate the use of similarity rank disclosure (SRD), an information-theoretic metric, which operates on feature representations rather than classifier decisions, providing a threshold-independent assessment of privacy and analysis of both average and worst-case disclosure. We report its application to speaker embeddings, fundamental frequency, and phone embeddings using 2024 VoicePrivacy Challenge systems. The SRD reveals privacy leaks and system-specific weaknesses missed by EER-based evaluation. Findings highlight the merit of representation-level metrics and demonstrate the potential of SRD as a flexible and interpretable tool for the evaluation of voice anonymisation.

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arXiv2026

Latent Secret Spin: Keyed Orthogonal Rotations for Blind Speech Watermarking in Anisotropic Latent Spaces

We introduce Latent Secret Spin (LSS), a blind speech watermarking method based on geometric operations in codec latent space. Based upon orthogonal rotations to principal components, LSS induces imperceptible but detectable covariance signatures according to a pseudo-random watermarking schedule. The scheme generalises across datasets, preserves perceptual quality and, unlike some learned, neural watermarking schemes, it does not require neural network training, is resistant to common signal manipulations and is flexible to payload size. Analyses show that structured latent-space watermarking is a promising and interpretable alternative to existing approaches.

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arXiv2026

Identity leakage through accent cues in voice anonymisation

Voice anonymisation is used to conceal voice identity while preserving linguistic content. Even if anonymisation seems strong, non-timbral cues such as accent that remain post-anonymisation can help re-identification and reveal sensitive socio-demographic traits. We report a study of residual accent information involving multiple anonymisation systems. We highlight the role of accent using speaker verification, accent verification, and accent classification using a set of embeddings focusing on timbral, non-timbral and accent-related information and show the extent to which related cues facilitate reidentification post anonymisation. Results show that, while some systems are robust to reidentification attempts using accent cues, others leave residual, speaker-dependent, accentrelated cues which can be used to reveal the voice identity. We also highlight accent-dependent variation in anonymisation performance, raising fairness concerns, and show that a system with characterlevel conditioning can help obfuscate identity-revealing accent cues, reducing accent-identification accuracy by 68% on average and improving overall anonymisation performance by 11% relative.

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arXiv2026

The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization

We present results and analyses from the third VoicePrivacy Challenge held in 2024, which focuses on advancing voice anonymization technologies. The task was to develop a voice anonymization system for speech data that conceals a speaker's voice identity while preserving linguistic content and emotional state. We provide a systematic overview of the challenge framework, including detailed descriptions of the anonymization task and datasets used for both system development and evaluation. We outline the attack model and objective evaluation metrics for assessing privacy protection (concealing speaker voice identity) and utility (content and emotional state preservation). We describe six baseline anonymization systems and summarize the innovative approaches developed by challenge participants. Finally, we provide key insights and observations to guide the design of future VoicePrivacy challenges and identify promising directions for voice anonymization research.

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arXiv2025

MDD: a Mask Diffusion Detector to Protect Speaker Verification Systems from Adversarial Perturbations

Speaker verification systems are increasingly deployed in security-sensitive applications but remain highly vulnerable to adversarial perturbations. In this work, we propose the Mask Diffusion Detector (MDD), a novel adversarial detection and purification framework based on a \textit{text-conditioned masked diffusion model}. During training, MDD applies partial masking to Mel-spectrograms and progressively adds noise through a forward diffusion process, simulating the degradation of clean speech features. A reverse process then reconstructs the clean representation conditioned on the input transcription. Unlike prior approaches, MDD does not require adversarial examples or large-scale pretraining. Experimental results show that MDD achieves strong adversarial detection performance and outperforms prior state-of-the-art methods, including both diffusion-based and neural codec-based approaches. Furthermore, MDD effectively purifies adversarially-manipulated speech, restoring speaker verification performance to levels close to those observed under clean conditions. These findings demonstrate the potential of diffusion-based masking strategies for secure and reliable speaker verification systems.

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arXiv2025

Reference-free Adversarial Sex Obfuscation in Speech

Sex conversion in speech involves privacy risks from data collection and often leaves residual sex-specific cues in outputs, even when target speaker references are unavailable. We introduce RASO for Reference-free Adversarial Sex Obfuscation. Innovations include a sex-conditional adversarial learning framework to disentangle linguistic content from sex-related acoustic markers and explicit regularisation to align fundamental frequency distributions and formant trajectories with sex-neutral characteristics learned from sex-balanced training data. RASO preserves linguistic content and, even when assessed under a semi-informed attack model, it significantly outperforms a competing approach to sex obfuscation.

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arXiv2025

The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation

Voice anonymisation aims to conceal the voice identity of speakers in speech recordings. Privacy protection is usually estimated from the difficulty of using a speaker verification system to re-identify the speaker post-anonymisation. Performance assessments are therefore dependent on the verification model as well as the anonymisation system. There is hence potential for privacy protection to be overestimated when the verification system is poorly trained, perhaps with mismatched data. In this paper, we demonstrate the insidious risk of overestimating anonymisation performance and show examples of exaggerated performance reported in the literature. For the worst case we identified, performance is overestimated by 74% relative. We then introduce a means to detect when performance assessment might be untrustworthy and show that it can identify all overestimation scenarios presented in the paper. Our solution is openly available as a fork of the 2024 VoicePrivacy Challenge evaluation toolkit.

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arXiv2024

2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems

We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Building upon the concept of 1D convolutional perturbations explored in the speech domain, our method leverages 2D convolutional filters to craft perturbations which significantly degrade the performance of state-of-the-art face deepfake detectors. Unlike traditional additive noise approaches, 2D-Malafide optimises a small number of filter coefficients to generate robust adversarial perturbations which are transferable across different face images. Experiments, conducted using the FaceForensics++ dataset, demonstrate that 2D-Malafide substantially degrades detection performance in both white-box and black-box settings, with larger filter sizes having the greatest impact. Additionally, we report an explainability analysis using GradCAM which illustrates how 2D-Malafide misleads detection systems by altering the image areas used most for classification. Our findings highlight the vulnerability of current deepfake detection systems to convolutional adversarial attacks as well as the need for future work to enhance detection robustness through improved image fidelity constraints.

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arXiv2024

Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model

We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to modify speech utterances, Malacopula enhances the effectiveness of spoofing attacks. The model comprises parallel branches of polynomial functions followed by linear time-invariant filters. The adversarial optimisation procedure acts to minimise the cosine distance between speaker embeddings extracted from spoofed and bona fide utterances. Experiments, performed using three recent ASV systems and the ASVspoof 2019 dataset, show that Malacopula increases vulnerabilities by a substantial margin. However, speech quality is reduced and attacks can be detected effectively under controlled conditions. The findings emphasise the need to identify new vulnerabilities and design defences to protect ASV systems from adversarial attacks in the wild.

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arXiv2024

Preserving spoken content in voice anonymisation with character-level vocoder conditioning

Voice anonymisation can be used to help protect speaker privacy when speech data is shared with untrusted others. In most practical applications, while the voice identity should be sanitised, other attributes such as the spoken content should be preserved. There is always a trade-off; all approaches reported thus far sacrifice spoken content for anonymisation performance. We report what is, to the best of our knowledge, the first attempt to actively preserve spoken content in voice anonymisation. We show how the output of an auxiliary automatic speech recognition model can be used to condition the vocoder module of an anonymisation system using a set of learnable embedding dictionaries in order to preserve spoken content. Relative to a baseline approach, and for only a modest cost in anonymisation performance, the technique is successful in decreasing the word error rate computed from anonymised utterances by almost 60%.

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arXiv2024

The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation

The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and datasets used for system development and evaluation, present the different attack models used for evaluation, and the associated objective and subjective metrics. We describe three anonymisation baselines, provide a summary description of the anonymisation systems developed by challenge participants, and report objective and subjective evaluation results for all. In addition, we describe post-evaluation analyses and a summary of related work reported in the open literature. Results show that solutions based on voice conversion better preserve utility, that an alternative which combines automatic speech recognition with synthesis achieves greater privacy, and that a privacy-utility trade-off remains inherent to current anonymisation solutions. Finally, we present our ideas and priorities for future VoicePrivacy Challenge editions.

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arXiv2024

The VoicePrivacy 2024 Challenge Evaluation Plan

The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the basis of the participants' requests. Participants apply their developed anonymization systems, run evaluation scripts and submit evaluation results and anonymized speech data to the organizers. Results will be presented at a workshop held in conjunction with Interspeech 2024 to which all participants are invited to present their challenge systems and to submit additional workshop papers.

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arXiv2024

Speaker anonymization using neural audio codec language models

The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform is resynthesized using a vocoder. Recent work has shown that x-vector transformations are difficult to control consistently: other sources of speaker information contained within fundamental frequency and linguistic features are re-entangled upon vocoding, meaning that anonymized speech signals still contain speaker information. We propose an approach based upon neural audio codecs (NACs), which are known to generate high-quality synthetic speech when combined with language models. NACs use quantized codes, which are known to effectively bottleneck speaker-related information: we demonstrate the potential of speaker anonymization systems based on NAC language modeling by applying the evaluation framework of the Voice Privacy Challenge 2022.

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arXiv2023

Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0

This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems. We adopt an approach that involves the fine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluating potential gender-related privacy vulnerabilities in the process. Gender influences during the fine-tuning process were employed to enhance fairness and privacy in order to emphasise or obscure gender information within the speakers' embeddings. Results from VoxCeleb datasets indicate our adversarial model increases privacy against uninformed attacks, yet slightly diminishes speaker verification performance compared to the non-adversarial model. However, the model's efficacy reduces against informed attacks. Analysis of system performance was conducted to identify potential gender biases, thus highlighting the need for further research to understand and improve the delicate interplay between utility, privacy, and equity in voice biometric systems.

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arXiv2023

Vocoder drift compensation by x-vector alignment in speaker anonymisation

For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which is used to substitute an original speaker x-vector with that of a fictitious pseudo-speaker. This phenomenon can impede the design of better anonymisation systems since there is a lack of fine-grained control over the x-vector space. The work reported in this paper explores the origin of so-called vocoder drift and shows that it is due to the mismatch between the substituted x-vector and the original representations of the linguistic content, intonation and prosody. Also reported is an original approach to vocoder drift compensation. While anonymisation performance degrades as expected, compensation reduces vocoder drift substantially, offers improved control over the x-vector space and lays a foundation for the design of better anonymisation functions in the future.

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