Leveraging distance information for generalized spoofing speech detection

Abstract

Spoofing speech detection (SSD) systems are confronted with insufficient generalization ability for in-the-wild data, including unseen attacks and bonafide speech from unseen distributions, which hampers their applicability in real-world scenarios. Such performance degradation could be attributed to the inherent flaw of deep neural network (DNN)-based models, that is, overlearning the training data. Inter-instance distance, which is underutilized in conventional DNN-based classifiers, proves beneficial in handling unseen samples. Our experiments indicate that the distances between bonafide speech are closer than spoofing one in certain feature spaces. Therefore, this paper proposes a distance-based method to enhance anti-spoofing models’ generalization ability. By incorporating distance features as a prefix, the proposed method achieves lightweight parameter updates while effectively detecting unseen attacks and bonafide utterances from unseen distributions. On the logical access of ASVspoof 2019 and ASVspoof 2021, the proposed method achieves 0.53% and 4.73% equal error rates (EERs). Moreover, it achieves 1.86% and 7.30% EERs on the ASVspoof 2021 Deepfake and IntheWild datasets, respectively, demonstrating its superior generalization ability. The proposed method outperforms other state-of-the-art (SOTA) methods on multiple datasets.