Paper
Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks
Authors
Pol Buitrago, Oriol Pareras, Federico Costa, Javier Hernando
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.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.08231v1</id>\n <title>Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks</title>\n <updated>2026-03-09T11:02:57Z</updated>\n <link href='https://arxiv.org/abs/2603.08231v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.08231v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.\n 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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.AS'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <published>2026-03-09T11:02:57Z</published>\n <arxiv:comment>6 pages, 5 figures, Submitted to Interspeech 2026</arxiv:comment>\n <arxiv:primary_category term='eess.AS'/>\n <author>\n <name>Pol Buitrago</name>\n </author>\n <author>\n <name>Oriol Pareras</name>\n </author>\n <author>\n <name>Federico Costa</name>\n </author>\n <author>\n <name>Javier Hernando</name>\n </author>\n </entry>"
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