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cs.SD2026

Towards Robust Uncertainty-Aware Speaker Modeling

Junjie Li, Yang Xiao, Kong Aik Lee

Speaker embeddings aggregate frame-level acoustic features into compact representations for speaker recognition. Recent uncertainty-aware speaker modeling approaches further charac…

cs.SD2026

QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection

Duc-Tuan Truong, Tianchi Liu, Ruijie Tao +3

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-ce…

cs.SD2026

U3-xi: Pushing the Boundaries of Speaker Recognition by Incorporating Uncertainty

Junjie Li, Kong Aik Lee

An utterance-level speaker embedding is typically obtained by aggregating a sequence of frame-level representations. However, in real-world scenarios, individual frames encode not…

cs.SD2026

Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection

Duc-Tuan Truong, Tianchi Liu, Junjie Li +3

In speech deepfake detection (SDD), data augmentation (DA) is commonly used to improve model generalization across varied speech conditions and spoofing attacks. However, during tr…

cs.SD2026

Xi+: Uncertainty Supervision for Robust Speaker Embedding

Junjie Li, Kong Aik Lee, Duc-Tuan Truong +2

There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Sinc…