  Voice anti-spoofing systems are crucial auxiliaries for automatic speaker
verification (ASV) systems. A major challenge is caused by unseen attacks
empowered by advanced speech synthesis technologies. Our previous research on
one-class learning has improved the generalization ability to unseen attacks by
compacting the bona fide speech in the embedding space. However, such
compactness lacks consideration of the diversity of speakers. In this work, we
propose speaker attractor multi-center one-class learning (SAMO), which
clusters bona fide speech around a number of speaker attractors and pushes away
spoofing attacks from all the attractors in a high-dimensional embedding space.
For training, we propose an algorithm for the co-optimization of bona fide
speech clustering and bona fide/spoof classification. For inference, we propose
strategies to enable anti-spoofing for speakers without enrollment. Our
proposed system outperforms existing state-of-the-art single systems with a
relative improvement of 38% on equal error rate (EER) on the ASVspoof2019 LA
evaluation set.
