34 citations · 41 across the 15 of their papers we have counts for
8 papers · 1 filter
Learning as Deepfakes Evolve: RF-Prompt for Continual Audio Deepfake Detection
Yuankun Xie, Xiaoxuan Guo, Xiaopeng Wang +3
Continual audio deepfake detection requires learning newly emerging deepfake methods while retaining discrimination of previously encountered speech. Existing dataset-incremental e…
Boundary and Intra-Segment Learning for Partial Audio Deepfake Localization
Zhe Ye, Xiangui Kang, Minhua Huang +3
Partial audio deepfakes manipulate only selected speech regions, making them difficult to be localized. Existing methods exploit boundary cues for partial deepfake localization, bu…
Not All Attacks Are Learned Equally in Speech Deepfake Detection
Avantika Singh, Aurosweta Mahapatra, Ismail Rasim Ulgen +3
Speech deepfake detection (SDD) models are trained on multi-attack datasets containing diverse spoofing systems, such as text-to-speech (TTS) and voice conversion (VC). In standard…
BiMTokenizer: Preserving Semantic-Acoustic Balance in Low-Bitrate Speech Tokenization via Bidirectional State-Space Modeling
Xin Zhang, Lin Li, Chuanbo Liu +2
Speech codecs serve as bridges between continuous speech signals and large language models, yet face an inherent conflict between acoustic fidelity and semantic preservation. To mi…
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…
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…
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…
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…