Shift to Your Device: Data Augmentation for Device-Independent Speaker Verification Anti-Spoofing

Junhao Wang, Li Lu, Zhongjie Ba, Feng Lin, Kui Ren

Abstract: This paper proposes a novel Deconvolution-enhanced data Augmentation method, DeAug, for ultrasonic-based speaker verification anti-spoofing systems to detect the liveness of voice sources in physical access, which aims to improve the performance of liveness detection on unseen devices where no data is collected yet. Specifically, DeAug first employs a wiener deconvolution pre-processing on available collected data to generate enhanced clean signal samples. Then, the generated samples are convolved with different device impulse responses, to enable the signal with the unseen devices' channel characteristics. Experiments on cross-domain datasets show that our proposed augmentation method can improve the performance of ultrasonic-based anti-spoofing systems by 97.8% relatively, and a further improvement of up to 43.4% can be obtained after applying domain adversarial training on multi-device augmented data.