  The ability of countermeasure models to generalize from seen speech synthesis
methods to unseen ones has been investigated in the ASVspoof challenge.
However, a new mismatch scenario in which fake audio may be generated from real
audio with unseen genres has not been studied thoroughly. To this end, we first
use five different vocoders to create a new dataset called CN-Spoof based on
the CN-Celeb1\&2 datasets. Then, we design two auxiliary objectives for
regularization via meta-optimization and a genre alignment module,
respectively, and combine them with the main anti-spoofing objective using
learnable weights for multiple loss terms. The results on our cross-genre
evaluation dataset for anti-spoofing show that the proposed method
significantly improved the generalization ability of the countermeasures
compared with the baseline system in the genre mismatch scenario.
