25 citations · 41 across the 14 of their papers we have counts for
7 papers · 1 filter
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
Xiaohui Zhang, Jiangyan Yi, Chenglong Wang +3
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective…
Fewer-token Neural Speech Codec with Time-invariant Codes
Yong Ren, Tao Wang, Jiangyan Yi +4
Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech code…
Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms
Chu Yuan Zhang, Jiangyan Yi, Jianhua Tao +2
Recent strides in neural speech synthesis technologies, while enjoying widespread applications, have nonetheless introduced a series of challenges, spurring interest in the defence…
Audio Deepfake Detection: A Survey
Jiangyan Yi, Chenglong Wang, Jianhua Tao +3
Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the prob…
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
Xiaohui Zhang, Jiangyan Yi, Jianhua Tao +2
Current fake audio detection algorithms have achieved promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a…
TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +4
Current fake audio detection relies on hand-crafted features, which lose information during extraction. To overcome this, recent studies use direct feature extraction from raw audi…
Detection of Cross-Dataset Fake Audio Based on Prosodic and Pronunciation Features
Chenglong Wang, Jiangyan Yi, Jianhua Tao +3
Existing fake audio detection systems perform well in in-domain testing, but still face many challenges in out-of-domain testing. This is due to the mismatch between the training a…