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arXiv · 2209.06434

ConvNeXt Based Neural Network for Audio Anti-Spoofing

Qiaowei Ma·Jinghui Zhong·Yitao Yang·Weiheng Liu·Ying Gao·Wing W. Y. Ng
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Abstract

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.

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BibTeXRIS

Qiaowei Ma, Jinghui Zhong, Yitao Yang, Weiheng Liu, Ying Gao, Wing W. Y. Ng. 2022-09-14. ConvNeXt Based Neural Network for Audio Anti-Spoofing. https://arxiv.org/abs/2209.06434

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