10 papers
AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks
Aurosweta Mahapatra, Xiutian Zhao, Shreeram Suresh Chandra +7
Speech deepfake detection (SDD) systems achieve strong performance on conventional benchmarks; however, existing datasets provide limited coverage of emotionally expressive and rec…
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…
RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
Hieu-Thi Luong, Xuechen Liu, Ivan Kukanov +2
RADAR Challenge 2026 is an APSIPA Grand Challenge on Robust Audio Deepfake Recognition under Media Transformations, designed to simulate realistic media conditions in real-world au…
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…
Stream-Voice-Anon: Enhancing Utility of Real-Time Speaker Anonymization via Neural Audio Codec and Language Models
Nikita Kuzmin, Songting Liu, Kong Aik Lee +1
Protecting speaker identity is crucial for online voice applications, yet streaming speaker anonymization (SA) remains underexplored. Recent research has demonstrated that neural a…
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…