  Existing approaches for replay and synthetic speech detection still lack
generalizability to unseen spoofing attacks. This work proposes to leverage a
novel model structure, so-called Res2Net, to improve the anti-spoofing
countermeasure's generalizability. Res2Net mainly modifies the ResNet block to
enable multiple feature scales. Specifically, it splits the feature maps within
one block into multiple channel groups and designs a residual-like connection
across different channel groups. Such connection increases the possible
receptive fields, resulting in multiple feature scales. This multiple scaling
mechanism significantly improves the countermeasure's generalizability to
unseen spoofing attacks. It also decreases the model size compared to
ResNet-based models. Experimental results show that the Res2Net model
consistently outperforms ResNet34 and ResNet50 by a large margin in both
physical access (PA) and logical access (LA) of the ASVspoof 2019 corpus.
Moreover, integration with the squeeze-and-excitation (SE) block can further
enhance performance. For feature engineering, we investigate the
generalizability of Res2Net combined with different acoustic features, and
observe that the constant-Q transform (CQT) achieves the most promising
performance in both PA and LA scenarios. Our best single system outperforms
other state-of-the-art single systems in both PA and LA of the ASVspoof 2019
corpus.
