DFKI-Speech系统用于WildSpoof挑战：一种针对SASV在野的鲁棒框架
DFKI-Speech System for WildSpoof Challenge: A robust framework for SASV In-the-Wild
AI总结：
DFKI-Speech系统通过自监督提取器与图神经网络结合，提出鲁棒的SASV框架，融合多尺度特征提升说话人验证性能。
摘要：
本文介绍了为WildSpoof挑战SASV赛道开发的DFKI-Speech系统。我们提出了一种鲁棒的SASV框架，其中反欺诈检测器和说话人验证（SV）网络协同工作。反欺诈检测器采用自监督语音嵌入提取器作为前端，结合最先进的图神经网络后端。此外，基于顶部3层的混合专家（MoE）用于融合高层和低层特征，以有效检测伪造语音。对于说话人验证，我们采用低复杂度的卷积神经网络，该网络在多个尺度上融合2D和1D特征，并使用SphereFace损失进行训练。此外，对比圆损失被应用于适应性地加权每个训练批次中的正负对，使网络能够更好地区分难样本和易样本。最后，基于固定冒充群体的AS Norm分数归一化和模型集成用于进一步增强说话人验证系统的辨别能力。
英文摘要：
This paper presents the DFKI-Speech system developed for the WildSpoof Challenge under the Spoofing aware Automatic Speaker Verification (SASV) track. We propose a robust SASV framework in which a spoofing detector and a speaker verification (SV) network operate in tandem. The spoofing detector employs a self-supervised speech embedding extractor as the frontend, combined with a state-of-the-art graph neural network backend. In addition, a top-3 layer based mixture-of-experts (MoE) is used to fuse high-level and low-level features for effective spoofed utterance detection. For speaker verification, we adapt a low-complexity convolutional neural network that fuses 2D and 1D features at multiple scales, trained with the SphereFace loss. Additionally, contrastive circle loss is applied to adaptively weight positive and negative pairs within each training batch, enabling the network to better distinguish between hard and easy sample pairs. Finally, fixed imposter cohort based AS Norm score normalization and model ensembling are used to further enhance the discriminative capability of the speaker verification system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
