SV-DeiT: Speaker Verification with DeiTCap Spoofing Detection

As advancements in automatic speech generation continue to progress, the ability to distinguish between real and fake samples has diminished. In addition, current spoofing detection algorithms struggle to perform well on new and unseen test distributions. To address these challenges, this paper presents two contributions. First, inspired by the success of transformer and capsule networks in high representation capabilities, we propose the DeiTCap spoof detection network on spectrogram audio features. This framework utilizes multi-head attention, sub-entities (capsules) in the audio domain and a modified routing algorithm to identify capsule agreement. The proposed spoof detection algorithm is integrated into the spoofing aware speaker recognition framework SV-DeiT. Second, we introduce a novel text-to-speech dataset TRADIF created with cutting-edge transformers and diffusion models to evaluate the generalizability of countermeasure systems. Our proposed DeiTCap achieves an EER of 1.08% on the evaluation set of the ASVSpoof2019 LA dataset. Moreover, the proposed network demonstrates strength in cross-domain training-testing with two different datasets, highlighting its robustness and versatility.