Improving Robustness of Speech Anti-Spoofing System Using Resnext with Neighbor Filters

Zhenyu Zhang, Xianfeng Zhao, Xiaowei Yi

Abstract: Since recent advances in speech synthesis techniques, it is important to develop robust speech anti-spoofing systems against all major spoofing attacks. In this paper, we propose a novel spoofing speech detection model by jointing ResNeXt with neighbor filters (NF-ResNeXt) to improve the robustness of speech anti-spoofing models. Inspired by higher-order cepstral coefficients are more difficult to be maintained during the speech synthesis procedure, we present a novel neighbor filter module for extracting the residual features to enhance the robustness of cepstral features. Then, we introduce a neural network architecture based on ResNeXt model for processing the residual features and calculating the scores of speech clips being spoofed. The NF-ResNeXt model is trained on the training set of ASVspoof 2019 logical access (LA) dataset and achieves an equal error rate (EER) of 5.13% on the evaluation dataset, which outperforms the existing state-of-the-art speech anti-spoofing models.