  Recent works on speech spoofing countermeasures still lack generalization
ability to unseen spoofing attacks. This is one of the key issues of ASVspoof
challenges especially with the rapid development of diverse and high-quality
spoofing algorithms. In this work, we address the generalizability of spoofing
detection by proposing prototypical loss under the meta-learning paradigm to
mimic the unseen test scenario during training. Prototypical loss with
metric-learning objectives can learn the embedding space directly and emerges
as a strong alternative to prevailing classification loss functions. We propose
an anti-spoofing system based on squeeze-excitation Residual network
(SE-ResNet) architecture with prototypical loss. We demonstrate that the
proposed single system without any data augmentation can achieve competitive
performance to the recent best anti-spoofing systems on ASVspoof 2019 logical
access (LA) task. Furthermore, the proposed system with data augmentation
outperforms the ASVspoof 2021 challenge best baseline both in the progress and
evaluation phase of the LA task. On ASVspoof 2019 and 2021 evaluation set LA
scenario, we attain a relative 68.4% and 3.6% improvement in min-tDCF compared
to the challenge best baselines, respectively.
