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With the rapid development of speech conversion and speech synthesisalgorithms, automatic speaker verification (ASV) systems are vulnerable tospoofing attacks. In recent years, researchers had proposed a number ofanti-spoofing methods based on hand-crafted features. However, usinghand-crafted features rather than raw waveform will lose implicit informationfor anti-spoofing. Inspired by the promising performance of ConvNeXt in imageclassification tasks, we revise the ConvNeXt network architecture and propose alightweight end-to-end anti-spoofing model. By integrating with the channelattention block and using the focal loss function, the proposed model can focuson the most informative sub-bands of speech representations and the difficultsamples that are hard to classify. Experiments show that our proposed systemcould achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for theASVSpoof 2019 LA evaluation dataset, which outperforms the state-of-the-artsystems.

ConvNeXt Based Neural Network for Audio Anti-Spoofing