用矩阵量化跨语言语音任务迁移效果，发现语言间影响有规律可循。
Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks
- 构建跨语言迁移矩阵，系统分析多语言间迁移关系
- 在性别识别与声纹验证中发现语言依赖性差异
- 适合研究跨语言语音模型泛化能力的学者
语音中的副语言任务通常被认为具有较低的语言依赖性，因其依赖非语言声学线索而非词汇内容。然而先前研究显示跨语言条件下性能下降，表明存在不可忽视的语言依赖性。但现有研究多聚焦单一语言对或特定任务设置，难以比较且无法系统评估任务层面的语言依赖性。本文提出跨语言迁移矩阵（CLTM），一种系统量化给定任务中语言对之间迁移效应的方法。我们利用基于多语言HuBERT的编码器，在性别识别和声纹验证两个任务上应用该方法，分析源语言数据对目标语言微调性能的影响。结果揭示了任务与语言间的明显迁移模式，反映出系统性的语言依赖效应。
原文摘要 · Abstract (English)
Paralinguistic speech tasks are often considered relatively language-agnostic, as they rely on extralinguistic acoustic cues rather than lexical content. However, prior studies report performance degradation under cross-lingual conditions, indicating non-negligible language dependence. Still, these studies typically focus on isolated language pairs or task-specific settings, limiting comparability and preventing a systematic assessment of task-level language dependence. We introduce the Cross-Lingual Transfer Matrix (CLTM), a systematic method to quantify cross-lingual interactions between pairs of languages within a given task. We apply the CLTM to two paralinguistic tasks, gender identification and speaker verification, using a multilingual HuBERT-based encoder, to analyze how donor-language data affects target-language performance during fine-tuning. Our results reveal distinct transfer patterns across tasks and languages, reflecting systematic, language-dependent effects.
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