解耦跨语言说话人验证中的说话人与语言效应：以伊比利亚语言为例
Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages
AI总结：
针对跨语言说话人验证中语言不匹配与说话人变异性混淆的问题，引入双语同说话人评估集，基于HuBERT系统分析发现语言不匹配是性能下降主因。
摘要：
跨语言说话人验证（SV）系统通常在注册和测试语音使用不同语言时表现出性能下降。然而，标准评估协议将语言不匹配与说话人间变异性混淆，因为评估通常使用不同语言的说话人。在这项工作中，我们为五种伊比利亚语言引入了一个双语同说话人评估集，从而能够在恒定说话人身份下分析跨语言SV。我们将此设置应用于先前显示出强烈语言依赖性的基于HuBERT的SV系统，并使用跨语言转移矩阵（CLTM）分析结果，以研究成对跨语言转移。我们的结果表明，与说话人相关的变异性解释了部分观察到的性能下降，但语言不匹配仍然是跨语言性能损失的主要驱动因素。这些发现为跨语言SV中的语言依赖性提供了更精确的表征。
英文摘要：
Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different languages. However, standard evaluation protocols confound language mismatch with inter-speaker variability, as evaluation is generally performed with different speakers across languages. In this work, we introduce a bilingual same-speaker evaluation set for five Iberian languages, enabling analysis of cross-lingual SV under constant speaker identity. We apply this setup to a HuBERT-based SV system previously shown to exhibit strong language dependence, and analyze results using the Cross-Lingual Transfer Matrix (CLTM) to study pairwise cross-lingual transfer. Our results show that speaker-related variability accounts for part of the observed degradation, but language mismatch remains the main driver of cross-lingual performance loss. These findings provide a more precise characterization of language dependence in cross-lingual SV.
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