研究不同语言对跨语言说话人验证的影响，发现训练语言会削弱其他语言的差异性。
On the influence of language similarity in non-target speaker verification trials
- 用ECAPA-TDNN模型在多语和单语VoxCeleb数据上训练，分析跨语言验证得分分布
- 训练语言内的说话人对比出现聚类现象，得分受比较语言影响小
- 非训练语言间得分与语言相似度相关，尤其在多语训练下更明显
本文利用先进的说话人验证系统ECAPA-TDNN，基于多语种和单语种VoxCeleb数据集进行训练，研究跨语言非目标说话人验证中语言相似性的影响。在多语种Globalphone和LDC CTS数据集上的分析显示，涉及训练语言的说话人比较存在聚类效应，比较语言的选择对得分影响甚微。相反，在未包含于训练集的语言之间，得分与由语言分类系统测得的语言相似度显著相关，尤其在使用多语种训练数据时更为明显。
原文摘要 · Abstract (English)
In this paper, we investigate the influence of language similarity in cross-lingual non-target speaker verification trials using a state-of-the-art speaker verification system, ECAPA-TDNN, trained on multilingual and monolingual variants of the VoxCeleb dataset. Our analysis of the score distribution patterns on multilingual Globalphone and LDC CTS reveals a clustering effect in speaker comparisons involving a training language, whereby the choice of comparison language only minimally impacts scores. Conversely, we observe a language similarity effect in trials involving languages not included in the training set of the speaker verification system, with scores correlating with language similarity measured by a language classification system, especially when using multilingual training data.
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