arXiv subjects

Yangyang Qu

Publications and source records attributed to Yangyang Qu.

10 recordsLinked to original sources

arXiv2026

PAID: Phoneme-Aligned Acoustic Units for Cross-Corpus L1-Background Classification

First-language (L1) background classification from second-language English speech aims to identify pronunciation-related acoustic cues that remain informative across speakers and corpora. Global pooling is commonly used to aggregate frame-level speech representations, but averaging features across an utterance may obscure local acoustic differences in how individual phonemes are pronounced. To model these pronunciation-related cues before utterance-level aggregation, we introduce Phoneme-Aligned Input Design (PAID), which constructs acoustic units from frozen Whisper representations using transcript-assisted forced alignment. PAID applies a shared nonlinear mapping to each acoustic unit before utterance-level aggregation and incorporates auxiliary phoneme-label embeddings. We construct a six-L1 dataset with speaker-disjoint partitions and evaluate cross-corpus performance on the Speech Accent Archive (SAA). PAID achieves 89.72% Macro-F1 on the source Test set and 80.14% on SAA. Matched comparisons show that phoneme-aligned acoustic-unit modeling improves SAA Macro-F1 by 13.44 percentage points over global pooling. To address uneven classification performance across L1 backgrounds, we additionally evaluate class-sensitive training, which improves observed weakest-class performance.

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arXiv2026

CLRM: Cross-Layer Relation Modeling for Audio Deepfake Detection

Audio deepfake detection (ADD) aims to distinguish bonafide speech from synthetic or manipulated speech. Representations from different layers of self-supervised learning (SSL) speech encoders vary in their ability to distinguish bonafide from spoofed speech. Comparing representations of the same utterance across encoder depths may reveal useful spoofing cues, yet common layer-selection and aggregation methods do not provide these comparisons as detection inputs. We propose Cross-Layer Relation Modeling (\ours{}), which explicitly encodes cross-layer relations alongside layer-specific features, making both directly available for ADD. CLRM reuses the existing detector's SSL encoder and identifies informative layer pairs by combining speaker-relative representation geometry with source-domain detection performance. The resulting relation score is fused with the detector's original score, requiring only the training of compact relation heads and fusion parameters while keeping the existing detector frozen. Across five external datasets, CLRM reduces the mean equal error rate (EER) for all three evaluated detectors, achieving relative reductions of 7.8--16.7%. Controlled comparisons with identical layer summaries and approximately matched auxiliary-head parameter counts also show a lower mean EER for explicit relations than direct concatenation, both before and after score fusion. These findings support explicit cross-layer comparisons as useful detection inputs for SSL-based audio deepfake detectors.

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arXiv2026

Shared-State Local Translations for Training-Free Voice Conversion

In one-shot training-free voice conversion (VC), the source and reference utterances may contain different linguistic content, so reliable frame-level correspondence between them cannot be assumed. We propose StateVC, which jointly defines a common set of local regions from pooled frame-level WavLM representations of the source and reference utterances; we refer to these regions as states. These shared states are obtained by fitting a pair-specific Gaussian mixture model to the pooled representations, without explicit source--reference frame matching. Within each state, StateVC estimates a source-to-reference mean shift in the original WavLM space. Source-frame posterior probabilities then combine the state-specific shifts so that different frames can receive different local updates. For the LibriSpeech one-shot protocol, StateVC achieves the lowest word error rate (WER) and character error rate (CER) among the evaluated systems, at 8.01% and 3.22%, respectively, with a speaker similarity (SIM) of 0.9512. It also achieves the highest mean perceived speaker similarity among the evaluated systems and the highest mean naturalness among the evaluated training-free systems.

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arXiv2026

Multi-sample Synthetic Supervision for Accent Conversion

Accent conversion (AC) requires changing accent while preserving speaker identity and linguistic content, yet parallel recordings are scarce. Speech synthesis provides an alternative source of supervision, but generated targets vary in accent realization and source preservation. We propose a multi-sample synthetic supervision framework that constructs conversion targets by jointly assessing these properties across candidate waveforms for each source--accent condition. Selected candidates provide associated discrete speech codes, representing linguistic content, prosody, and speaking style, as targets for accent-conditioned autoregressive adaptation. Compared with using one generated target per training example, our method improves target-accent classification accuracy by 2.49 percentage points, while speaker similarity remains nearly unchanged, and word error rate increases by 0.29 percentage points. Across six target accents, our method achieves 7.81% WER and the highest mean listening ratings among evaluated systems.

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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

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

Subject Information Extraction for Novelty Detection with Domain Shifts

Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industrial quality control. Most existing UND methods assume that the training data and testing normal data originate from the same domain and only consider the distribution variation between training data and testing data. However, in real scenarios, it is common for normal testing and training data to originate from different domains, a challenge known as domain shift. The discrepancies between training and testing data often lead to incorrect classification of normal data as novel by existing methods. A typical situation is that testing normal data and training data describe the same subject, yet they differ in the background conditions. To address this problem, we introduce a novel method that separates subject information from background variation encapsulating the domain information to enhance detection performance under domain shifts. The proposed method minimizes the mutual information between the representations of the subject and background while modelling the background variation using a deep Gaussian mixture model, where the novelty detection is conducted on the subject representations solely and hence is not affected by the variation of domains. Extensive experiments demonstrate that our model generalizes effectively to unseen domains and significantly outperforms baseline methods, especially under substantial domain shifts between training and testing data.

cs.CV↗

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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arXiv2020

UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement

Low-light image enhancement, such as recovering color and texture details from low-light images, is a complex and vital task. For automated driving, low-light scenarios will have serious implications for vision-based applications. To address this problem, we propose a real-time unsupervised generative adversarial network (GAN) containing multiple discriminators, i.e. a multi-scale discriminator, a texture discriminator, and a color discriminator. These distinct discriminators allow the evaluation of images from different perspectives. Further, considering that different channel features contain different information and the illumination is uneven in the image, we propose a feature fusion attention module. This module combines channel attention with pixel attention mechanisms to extract image features. Additionally, to reduce training time, we adopt a shared encoder for the generator and the discriminator. This makes the structure of the model more compact and the training more stable. Experiments indicate that our method is superior to the state-of-the-art methods in qualitative and quantitative evaluations, and significant improvements are achieved for both autopilot positioning and detection results.

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arXiv2020

LEUGAN:Low-Light Image Enhancement by Unsupervised Generative Attentional Networks

Restoring images from low-light data is a challenging problem. Most existing deep-network based algorithms are designed to be trained with pairwise images. Due to the lack of real-world datasets, they usually perform poorly when generalized in practice in terms of loss of image edge and color information. In this paper, we propose an unsupervised generation network with attention-guidance to handle the low-light image enhancement task. Specifically, our network contains two parts: an edge auxiliary module that restores sharper edges and an attention guidance module that recovers more realistic colors. Moreover, we propose a novel loss function to make the edges of the generated images more visible. Experiments validate that our proposed algorithm performs favorably against state-of-the-art methods, especially for real-world images in terms of image clarity and noise control.

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