A robust audio deepfake detection system via multi-view feature
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)
摘要
With the advancement of generative modeling techniques, synthetic human
speech becomes increasingly indistinguishable from real, and tricky challenges
are elicited for the audio deepfake detection (ADD) system. In this paper, we
exploit audio features to improve the generalizability of ADD systems.
Investigation of the ADD task performance is conducted over a broad range of
audio features, including various handcrafted features and learning-based
features. Experiments show that learning-based audio features pretrained on a
large amount of data generalize better than hand-crafted features on
out-of-domain scenarios. Subsequently, we further improve the generalizability
of the ADD system using proposed multi-feature approaches to incorporate
complimentary information from features of different views. The model trained
on ASV2019 data achieves an equal error rate of 24.27% on the In-the-Wild
dataset.
更多查看译文
关键词
Audio deepfake detection,anti-spoofing,feature incorporation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要