论文标题
两种用于欺骗意识的扬声器验证的方法：多层感知器得分融合模型和集成的嵌入式投影仪
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
论文作者
论文摘要
在过去的十年中，深度神经网络（DNN）的使用显着提高了自动扬声器验证（ASV）的性能。但是，通过欺骗攻击可以轻松地中和ASV系统。因此，设计和举行了欺骗意识的说话者验证（SASV）挑战，目的是促进可以通过集成ASV和欺骗对策（CM）系统来执行ASV的系统的开发。在本文中，我们提出了两个后端系统：多层感知器得分融合模型（MSFM）和集成的嵌入式投影仪（IEP）。 MSFM，得分融合后端系统，利用ASV和CM分数和嵌入的SASV得分得出。另一方面，IEP将ASV和CM嵌入到SASV嵌入中，并根据余弦相似性计算最终的SASV得分。我们通过提出的MSFM和IEP有效地整合了ASV和CM系统，并在SASV 2022挑战的官方评估试验中实现了SASV等于0.56％，1.32％。
The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neutralized by spoofing attacks. Therefore, the Spoofing-Aware Speaker Verification (SASV) challenge is designed and held to promote development of systems that can perform ASV considering spoofing attacks by integrating ASV and spoofing countermeasure (CM) systems. In this paper, we propose two back-end systems: multi-layer perceptron score fusion model (MSFM) and integrated embedding projector (IEP). The MSFM, score fusion back-end system, derived SASV score utilizing ASV and CM scores and embeddings. On the other hand,IEP combines ASV and CM embeddings into SASV embedding and calculates final SASV score based on the cosine similarity. We effectively integrated ASV and CM systems through proposed MSFM and IEP and achieved the SASV equal error rates 0.56%, 1.32% on the official evaluation trials of the SASV 2022 challenge.
