论文标题
在说话者识别中PLDA的域改编的广义框架
A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
论文作者
论文摘要
本文提出了一个通用框架，用于在说话者识别中适应概率线性判别分析（PLDA）的域。它不仅包括几种现有的监督和无监督的域适应方法，而且还包括在不同域中更灵活地使用可用数据。特别是，我们在这里介绍下面描述的两种新技术。 （1）基于相关对齐的插值和（2）协方差正则化。与在适应前的室外PLDA模型相比，基于相关 - 基准的插值方法可将MinCprimary降低到30.5％，而Mincprimary也比传统的线性插值方法低5.5％。此外，提出的正则化技术可确保插值W.R.T.的鲁棒性不同的插值权重，实际上这是必不可少的。
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here the two new techniques described below. (1) Correlation-alignment-based interpolation and (2) covariance regularization. The proposed correlation-alignment-based interpolation method decreases minCprimary up to 30.5% as compared with that from an out-of-domain PLDA model before adaptation, and minCprimary is also 5.5% lower than with a conventional linear interpolation method with optimal interpolation weights. Further, the proposed regularization technique ensures robustness in interpolations w.r.t. varying interpolation weights, which in practice is essential.
