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.

本文提出了一种基于 ConvNeXt 网络架构的轻量级端到端反欺骗模型，通过加入通道注意力块和使用 focal loss 函数，我们的模型可以专注于最具信息量的语音表示的子带和难以分类的样本，实验表明我们的系统在 ASVSpoof 2019 LA 评估数据集中可以实现 0.64％的等误差率和 0.0187 的 min-tDCF，超过了当前最先进系统。