10 papers · 1 filter
ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection
Hao Gu, Jiangyan Yi, Chenglong Wang +6
Audio deepfake detection (ADD) has grown increasingly important due to the rise of high-fidelity audio generative models and their potential for misuse. Given that audio large lang…
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
Yujie Chen, Jiangyan Yi, Cunhang Fan +10
Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although…
Audio Deepfake Attribution: An Initial Dataset and Investigation
Xinrui Yan, Jiangyan Yi, Jianhua Tao +1
The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manipulation of content. This has led to an increase in studies aimed…
Utilizing Speaker Profiles for Impersonation Audio Detection
Hao Gu, JiangYan Yi, Chenglong Wang +5
Fake audio detection is an emerging active topic. A growing number of literatures have aimed to detect fake utterance, which are mostly generated by Text-to-speech (TTS) or voice c…
EmoFake: An Initial Dataset for Emotion Fake Audio Detection
Yan Zhao, Jiangyan Yi, Jianhua Tao +3
Many datasets have been designed to further the development of fake audio detection, such as datasets of the ASVspoof and ADD challenges. However, these datasets do not consider a…
Spatial Reconstructed Local Attention Res2Net with F0 Subband for Fake Speech Detection
Cunhang Fan, Jun Xue, Jianhua Tao +4
The rhythm of bonafide speech is often difficult to replicate, which causes that the fundamental frequency (F0) of synthetic speech is significantly different from that of real spe…