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TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
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Current fake audio detection relies on hand-crafted features, which lose information during extraction. To overcome this, recent studies use direct feature extraction from raw audio signals. For example, RawNet is one of the representative works in end-to-end fake audio detection. However, existing work on RawNet does not optimize the parameters of the Sinc-conv during training, which limited its performance. In this paper, we propose to incorporate orthogonal convolution into RawNet, which reduces the correlation between filters when optimizing the parameters of Sinc-conv, thus improving discriminability. Additionally, we introduce temporal convolutional networks (TCN) to capture long-term dependencies in speech signals. Experiments on the ASVspoof 2019 show that the Our TO-RawNet system can relatively reduce EER by 66.09\% on logical access scenario compared with the RawNet, demonstrating its effectiveness in detecting fake audio attacks.
Forward citations
Cited by 4 Pith papers
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RawNetLite, a lightweight raw-waveform model, reaches near-perfect F1 on FakeOrReal, but the headline out-of-distribution result uses test splits of datasets that were partly included in training.
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FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
FADEL replaces softmax with a Dirichlet-based evidential output in anti-spoofing models, improving EER on ASVspoof2019 LA and ASVspoof2021 LA over baseline backbones.
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