  With the rapid development of speech conversion and speech synthesis
algorithms, automatic speaker verification (ASV) systems are vulnerable to
spoofing attacks. In recent years, researchers had proposed a number of
anti-spoofing methods based on hand-crafted features. However, using
hand-crafted features rather than raw waveform will lose implicit information
for anti-spoofing. Inspired by the promising performance of ConvNeXt in image
classification tasks, we revise the ConvNeXt network architecture and propose a
lightweight end-to-end anti-spoofing model. By integrating with the channel
attention block and using the focal loss function, the proposed model can focus
on the most informative sub-bands of speech representations and the difficult
samples that are hard to classify. Experiments show that our proposed system
could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the
ASVSpoof 2019 LA evaluation dataset, which outperforms the state-of-the-art
systems.
