论文标题
关于说话者认可的最佳分数的评论
Remarks on Optimal Scores for Speaker Recognition
论文作者
论文摘要
在本文中，我们首先建立了说话者认可的最佳分数理论。我们的分析表明，说话者识别和说话者验证任务的最小贝叶斯风险（MBR）决定都可以基于标准化的可能性（NL）。当基础生成模型是线性高斯时，NL得分在数学上等同于PLDA的可能性比，并且基于余弦距离和欧几里得距离的经验得分可以看作是在某些条件下这种线性高斯NL分数的近似值。我们讨论了NL分数的许多属性，并执行一个简单的仿真实验，以证明NL分数的属性。
In this article, we first establish the theory of optimal scores for speaker recognition. Our analysis shows that the minimum Bayes risk (MBR) decisions for both the speaker identification and speaker verification tasks can be based on a normalized likelihood (NL). When the underlying generative model is a linear Gaussian, the NL score is mathematically equivalent to the PLDA likelihood ratio, and the empirical scores based on cosine distance and Euclidean distance can be seen as approximations of this linear Gaussian NL score under some conditions. We discuss a number of properties of the NL score and perform a simple simulation experiment to demonstrate the properties of the NL score.
