Improving Robustness of Speech Anti-Spoofing System Using Resnext with Neighbor Filters
IEEE International Conference on Multimedia and Expo (ICME)(2022)
摘要
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.
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关键词
neighbor filters,robustness,anti-spoofing
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