Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks

Pol Buitrago; Oriol Pareras; Federico Costa; Javier Hernando

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arXiv · 2603.08231

Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks

Pol Buitrago·Oriol Pareras·Federico Costa·Javier Hernando

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Abstract

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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Pol Buitrago, Oriol Pareras, Federico Costa, Javier Hernando. 2026-03-09. Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks. https://arxiv.org/abs/2603.08231

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