论文标题
珊瑚++算法，用于无监督的域名识别域的适应
The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion
论文作者
论文摘要
最先进的说话者识别系统接受了大量人体标记的培训数据集培训。这样的训练集通常由各种数据源组成，以增强模型的建模能力。但是，在实际部署中，看不见的状况几乎是不可避免的。由于培训和测试数据集之间的统计差异，域不匹配是现实生活应用中的一个常见问题。为了减轻域不匹配引起的退化，我们提出了一种新的基于特征的无监督域适应算法。我们提出的算法是基于众所周知的相关比对（珊瑚）的进一步优化，因此我们称其为珊瑚++。在NIST 2019发言人识别评估（SRE19）上，我们使用SRE18 CTS作为开发设置来验证珊瑚++的有效性。使用典型的X-Vector/PLDA设置，珊瑚++在EER上相对优于9.40％。
State-of-the-art speaker recognition systems are trained with a large amount of human-labeled training data set. Such a training set is usually composed of various data sources to enhance the modeling capability of models. However, in practical deployment, unseen condition is almost inevitable. Domain mismatch is a common problem in real-life applications due to the statistical difference between the training and testing data sets. To alleviate the degradation caused by domain mismatch, we propose a new feature-based unsupervised domain adaptation algorithm. The algorithm we propose is a further optimization based on the well-known CORrelation ALignment (CORAL), so we call it CORAL++. On the NIST 2019 Speaker Recognition Evaluation (SRE19), we use SRE18 CTS set as the development set to verify the effectiveness of CORAL++. With the typical x-vector/PLDA setup, the CORAL++ outperforms the CORAL by 9.40% relatively on EER.
