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QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection
Duc-Tuan Truong, Tianchi Liu, Ruijie Tao, Junjie Li, Kong Aik Lee, Eng Siong Chng
https://arxiv.org/abs/2509.20679

QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection
Recent work shows that one-class learning can detect unseen deepfake attacks by modeling a compact distribution of bona fide speech around a single centroid. However, the single-centroid assumption can oversimplify the bona fide speech representation and overlook useful cues, such as speech quality, which reflects the naturalness of the speech. Speech quality can be easily obtained using existing speech quality assessment models that estimate it through Mean Opinion Score. In this paper, we pro…

Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection
Duc-Tuan Truong, Tianchi Liu, Junjie Li, Ruijie Tao, Kong Aik Lee, Eng Siong Chng
https://arxiv.org/abs/2509.20682

Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection
In speech deepfake detection (SDD), data augmentation (DA) is commonly used to improve model generalization across varied speech conditions and spoofing attacks. However, during training, the backpropagated gradients from original and augmented inputs may misalign, which can result in conflicting parameter updates. These conflicts could hinder convergence and push the model toward suboptimal solutions, thereby reducing the benefits of DA. To investigate and address this issue, we design a dual-…

A Study of the Removability of Speaker-Adversarial Perturbations
Liping Chen, Chenyang Guo, Kong Aik Lee, Zhen-Hua Ling, Wu Guo
https://arxiv.org/abs/2510.09504 https://

A Study of the Removability of Speaker-Adversarial Perturbations
Recent advancements in adversarial attacks have demonstrated their effectiveness in misleading speaker recognition models, making wrong predictions about speaker identities. On the other hand, defense techniques against speaker-adversarial attacks focus on reducing the effects of speaker-adversarial perturbations on speaker attribute extraction. These techniques do not seek to fully remove the perturbations and restore the original speech. To this end, this paper studies the removability of spe…

Investigation of perception inconsistency in speaker embedding for asynchronous voice anonymization
Rui Wang, Liping Chen, Kong Aik Lee, Zhengpeng Zha, Zhenhua Ling
https://arxiv.org/abs/2510.05718

Investigation of perception inconsistency in speaker embedding for asynchronous voice anonymization
Given the speech generation framework that represents the speaker attribute with an embedding vector, asynchronous voice anonymization can be achieved by modifying the speaker embedding derived from the original speech. However, the inconsistency between machine and human perceptions of the speaker attribute within the speaker embedding remains unexplored, limiting its performance in asynchronous voice anonymization. To this end, this study investigates this inconsistency via modifications to s…