4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
GenTraceBench: A Benchmark for Tracing Audio Deepfakes Across Pre- and Post-training Stages
Li Wang, Kunyu Feng, Wan Lin +7
Modern text-to-speech (TTS) systems are rarely deployed as unchanged pre-trained models. They are often adapted through supervised fine-tuning (SFT) or preference optimization such…
CoReLoop: Parameter-Efficient Controlled Recurrent Refinement for Audio Deepfake Detection
Kunyu Feng, Yuxiang Wang, Li Wang +2
Audio deepfake detectors are expected to remain robust to diverse attacks, datasets, and recording conditions, yet collecting training data that cover every future condition is imp…
Teffic-Audio: Tell Fact from Fiction
Wan Lin, Li Wang, Jindong Wang +2
Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice conversion, vocoder reconstruction, and neura…
VoxSafeBench: Not Just What Is Said, but Who, How, and Where
Yuxiang Wang, Hongyu Liu, Yijiang Xu +9
As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than the words alone. Who is speak…
Neural Scoring: A Refreshed End-to-End Approach for Speaker Recognition in Complex Conditions
Wan Lin, Junhui Chen, Tianhao Wang +3
Modern speaker verification systems primarily rely on speaker embeddings, followed by verification based on cosine similarity between the embedding vectors of the enrollment and te…