arXiv subjects

Nicholas Evans

Publications and source records attributed to Nicholas Evans.

At least 19 recordsLinked to original sources

arXiv2026

Phoneme-Aware Pronunciation Representations for L2-English L1-Background Accent Identification

We study speaker-disjoint accent identification for L2 English, where the goal is to predict a speaker's first-language (L1) background from English pronunciation. Most existing systems classify accents using a single utterance-level representation, but such global representations can obscure pronunciation cues that depend on specific English phonemes. We propose a transcript-assisted model that makes phoneme information explicit during accent identification. Instead of representing an utterance only as a global speech embedding, we represent it as a sequence of pronunciation units, each combining acoustic evidence from a spoken segment with the aligned English phoneme for that segment. A frozen speech encoder provides the acoustic features, while the transcript is used only to obtain phoneme-level forced alignments. No word-level or sentence-level text representation is passed to the accent classifier. Under a four-fold speaker-disjoint protocol on L2-ARCTIC, our model achieves 81.41% accuracy and 81.21% macro-F1, the highest mean performance among the evaluated systems. Diagnostic ablations support the importance of phoneme-aligned token construction, while a Whisper-based ablation shows an additional gain from phoneme information.

eess.AS↗

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.

eess.AS↗

arXiv2026

Linguistic Bias Mitigation for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

Rapid advancements in generative speech technology have compromised the reliability of voice biometrics. While current spoofing detectors excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. We show that this can be due to linguistic bias. A reliance on linguistic cues observed in training data can then compromise robustness to cross-data. We propose a linguistic-invariant spoofing detection framework utilizing teacher-student adversarial learning. The linguistic-aware teacher model, pre-trained on linguistic content of an external dataset, guides the student detector via gradient reversal to minimize the linguistic information. To prevent the inadvertent removal of non-linguistic cues, we incorporate a Variational Information Bottleneck to enable suppression of principal cues. Across nine DF Arena datasets, our method achieves up to a 36.2% relative reduction in the EER compare to the baseline.

cs.CL↗

arXiv2026

A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends. Through multi-corpus training across three scenarios and six evaluation datasets, our empirical analysis yields two critical findings. First, we expose a domain bias within the ASVspoof 5 dataset, showing that naive data scaling actively degrades performance. Second, our cross-linguistic analysis reveals that fine-tuning with just 8 hours of target-language data enhances detection robustness. Together, these findings emphasize the critical need for domain-aware and language-specific adaptation in spoofing detection.

cs.SD↗

arXiv2026

Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. In this paper, we show that such issues could be caused by speaker bias, where models learn individual voice traits rather than markers of manipulation or generation. We propose a teacher-student framework for speaker-invariant spoofing detection that disentangles identity without requiring speaker labels. We leverage a pre-trained speaker recognition teacher to guide a student model via a gradient reversal layer. To control the balance between suppressing cues related to voice identity with the preservation of those related to spoofing detection, we integrate a Variational Information Bottleneck. Evaluations across nine datasets show our model achieves a 25.7% relative reduction to the EER compared to the MHFA baseline.

cs.SD↗

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.

eess.AS↗

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.

eess.AS↗

arXiv2026

Robust Accent Identification via Voice Conversion and Non-Timbral Embeddings

Automatic accent identification (AID) remains a challenging task due to the complex variability of accents, the entanglement of accent cues with speaker traits, and the scarcity of reliable accentlabelled data. To address these challenges, we propose a speaker augmentation strategy using voice conversion (VC), with which we generate additional training data by converting original training utterances into different speaker voices while preserving accentual cues. For this purpose, we select two recent VC systems and evaluate their capability to preserve accent. Alternatively, we also explore the use of non-timbral embeddings in AID, for their ability to convey accent information among other non timbral cues. The effectiveness of both methods is demonstrated on the GenAID benchmark, achieving a new state-of-the-art F1-score of 0.66, compared to the previous score of 0.55. Beyond AID, we show that non-timbral embeddings enable accent-controlled Text-to-Speech, producing high-fidelity speech with accurate accent transfer.

eess.SP↗

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.

eess.SP↗

arXiv2026

Enhancing Multi-Corpus Training in SSL-Based Anti-Spoofing Models: Domain-Invariant Feature Extraction

The performance of speech spoofing detection often varies across different training and evaluation corpora. Leveraging multiple corpora typically enhances robustness and performance in fields like speaker recognition and speech recognition. However, our spoofing detection experiments show that multi-corpus training does not consistently improve performance and may even degrade it. We hypothesize that dataset-specific biases impair generalization, leading to performance instability. To address this, we propose an Invariant Domain Feature Extraction (IDFE) framework, employing multi-task learning and a gradient reversal layer to minimize corpus-specific information in learned embeddings. The IDFE framework reduces the average equal error rate by 20% compared to the baseline, assessed across four varied datasets.

cs.LG↗

arXiv2026

Fair-Gate: Fairness-Aware Interpretable Risk Gating for Sex-Fair Voice Biometrics

Voice biometric systems can exhibit sex-related performance gaps even when overall verification accuracy is strong. We attribute these gaps to two practical mechanisms: (i) demographic shortcut learning, where speaker classification training exploits spurious correlations between sex and speaker identity, and (ii) feature entanglement, where sex-linked acoustic variation overlaps with identity cues and cannot be removed without degrading speaker discrimination. We propose Fair-Gate, a fairness-aware and interpretable risk-gating framework that addresses both mechanisms in a single pipeline. Fair-Gate applies risk extrapolation to reduce variation in speaker-classification risk across proxy sex groups, and introduces a local complementary gate that routes intermediate features into an identity branch and a sex branch. The gate provides interpretability by producing an explicit routing mask that can be inspected to understand which features are allocated to identity versus sex-related pathways. Experiments on VoxCeleb1 show that Fair-Gate improves the utility--fairness trade-off, yielding more sex-fair ASV performance under challenging evaluation conditions.

cs.SD↗

arXiv2026

Assessing the Impact of Speaker Identity in Speech Spoofing Detection

Spoofing detection systems are typically trained using diverse recordings from multiple speakers, often assuming that the resulting embeddings are independent of speaker identity. However, this assumption remains unverified. In this paper, we investigate the impact of speaker information on spoofing detection systems. We propose two approaches within our Speaker-Invariant Multi-Task framework, one that models speaker identity within the embeddings and another that removes it. SInMT integrates multi-task learning for joint speaker recognition and spoofing detection, incorporating a gradient reversal layer. Evaluated using four datasets, our speaker-invariant model reduces the average equal error rate by 17% compared to the baseline, with up to 48% reduction for the most challenging attacks (e.g., A11).

cs.SD↗

arXiv2026

WeDefense: A Toolkit to Defend Against Fake Audio

The advances in generative AI have enabled the creation of synthetic audio which is perceptually indistinguishable from real, genuine audio. Although this stellar progress enables many positive applications, it also raises risks of misuse, such as for impersonation, disinformation and fraud. Despite a growing number of open-source fake audio detection codes released through numerous challenges and initiatives, most are tailored to specific competitions, datasets or models. A standardized and unified toolkit that supports the fair benchmarking and comparison of competing solutions with not just common databases, protocols, metrics, but also a shared codebase, is missing. To address this, we propose WeDefense, the first open-source toolkit to support both fake audio detection and localization. Beyond model training, WeDefense emphasizes critical yet often overlooked components: flexible input and augmentation, calibration, score fusion, standardized evaluation metrics, and analysis tools for deeper understanding and interpretation. The toolkit is publicly available at https://github.com/zlin0/wedefense with interactive demos for fake audio detection and localization.

cs.SD↗

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.

cs.CL↗

arXiv2026

ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake detection solutions. A significant change from previous challenge editions is a new crowdsourced database collected from a substantially greater number of speakers under diverse recording conditions, and a mix of cutting-edge and legacy generative speech technology. With the new database described elsewhere, we provide in this paper an overview of the ASVspoof 5 challenge results for the submissions of 53 participating teams. While many solutions perform well, performance degrades under adversarial attacks and the application of neural encoding/compression schemes. Together with a review of post-challenge results, we also report a study of calibration in addition to other principal challenges and outline a road-map for the future of ASVspoof.

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arXiv2025

Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization

Ensuring accurate localization of robots in environments without GPS capability is a challenging task. Visual Place Recognition (VPR) techniques can potentially achieve this goal, but existing RGB-based methods are sensitive to changes in illumination, weather, and other seasonal changes. Existing cross-modal localization methods leverage the geometric properties of RGB images and 3D LiDAR maps to reduce the sensitivity issues highlighted above. Currently, state-of-the-art methods struggle in complex scenes, fine-grained or high-resolution matching, and situations where changes can occur in viewpoint. In this work, we introduce a framework we call Semantic-Enhanced Cross-Modal Place Recognition (SCM-PR) that combines high-level semantics utilizing RGB images for robust localization in LiDAR maps. Our proposed method introduces: a VMamba backbone for feature extraction of RGB images; a Semantic-Aware Feature Fusion (SAFF) module for using both place descriptors and segmentation masks; LiDAR descriptors that incorporate both semantics and geometry; and a cross-modal semantic attention mechanism in NetVLAD to improve matching. Incorporating the semantic information also was instrumental in designing a Multi-View Semantic-Geometric Matching and a Semantic Consistency Loss, both in a contrastive learning framework. Our experimental work on the KITTI and KITTI-360 datasets show that SCM-PR achieves state-of-the-art performance compared to other cross-modal place recognition methods.

cs.CV↗

arXiv2025

Exploring Situated Stabilities of a Rhythm Generation System through Variational Cross-Examination

This paper investigates GrooveTransformer, a real-time rhythm generation system, through the postphenomenological framework of Variational Cross-Examination (VCE). By reflecting on its deployment across three distinct artistic contexts, we identify three stabilities: an autonomous drum accompaniment generator, a rhythmic control voltage sequencer in Eurorack format, and a rhythm driver for a harmonic accompaniment system. The versatility of its applications was not an explicit goal from the outset of the project. Thus, we ask: how did this multistability emerge? Through VCE, we identify three key contributors to its emergence: the affordances of system invariants, the interdisciplinary collaboration, and the situated nature of its development. We conclude by reflecting on the viability of VCE as a descriptive and analytical method for Digital Musical Instrument (DMI) design, emphasizing its value in uncovering how technologies mediate, co-shape, and are co-shaped by users and contexts.

cs.HC↗

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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