This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed. Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge. We analyze that the inferior performance of single SASV embeddings comes from insufficient amount of training data and distinct nature of ASV and CM tasks. To this end, we propose a novel framework that includes multi-stage training and a combination of loss functions. Copy synthesis, combined with several vocoders, is also exploited to address the lack of spoofed data. Experimental results show dramatic improvements, achieving a SASV-EER of 1.06% on the evaluation protocol of the SASV2022 challenge.

翻译：本研究旨在开发一种单一的集成防欺骗说话人验证（SASV）嵌入，需满足两个方面的要求。首先，必须同时拒绝非目标说话人的输入以及目标说话人的欺骗性输入。其次，与自动说话人验证（ASV）和反欺骗（CM）嵌入的融合方案相比，应展现出具有竞争力的性能——该融合方案在SASV2022挑战赛中以较大优势超越了单一嵌入解决方案。我们分析认为，单一SASV嵌入性能不佳的原因在于训练数据量不足以及ASV与CM任务的本质差异。为此，我们提出了一种包含多阶段训练和组合损失函数的新框架。同时，利用多种声码器结合拷贝合成技术来解决欺骗数据匮乏的问题。实验结果显示性能显著提升，在SASV2022挑战赛的评估协议上实现了1.06%的SASV等错误率。