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        <title>Language Family Matters: Evaluating SpeechLLMs Across Linguistic Boundaries</title>
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    <abstract>Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a frozen speech encoder to a pretrained LLM via a lightweight connector. Prior work trains a separate connector per language, overlooking linguistic relatedness. We propose an efficient and novel connector-sharing strategy based on linguistic family membership, enabling one connector per family, and empirically validate its effectiveness across two multilingual LLMs and two real-world corpora spanning curated and crowd-sourced speech. Our results show that family-based connectors reduce parameter count while improving generalization across domains, offering a practical and scalable strategy for multilingual ASR deployment.</abstract>
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%0 Conference Proceedings
%T Language Family Matters: Evaluating SpeechLLMs Across Linguistic Boundaries
%A Zhang, Yuchen
%A Shekhar, Ravi
%A Mouratidis, Haralambos
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F zhang-etal-2026-language
%X Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a frozen speech encoder to a pretrained LLM via a lightweight connector. Prior work trains a separate connector per language, overlooking linguistic relatedness. We propose an efficient and novel connector-sharing strategy based on linguistic family membership, enabling one connector per family, and empirically validate its effectiveness across two multilingual LLMs and two real-world corpora spanning curated and crowd-sourced speech. Our results show that family-based connectors reduce parameter count while improving generalization across domains, offering a practical and scalable strategy for multilingual ASR deployment.
%U https://aclanthology.org/2026.eacl-short.36/
%P 487-499

Markdown (Informal)

[Language Family Matters: Evaluating SpeechLLMs Across Linguistic Boundaries](https://aclanthology.org/2026.eacl-short.36/) (Zhang et al., EACL 2026)

• Language Family Matters: Evaluating SpeechLLMs Across Linguistic Boundaries (Zhang et al., EACL 2026)

ACL

• Yuchen Zhang, Ravi Shekhar, and Haralambos Mouratidis. 2026. Language Family Matters: Evaluating SpeechLLMs Across Linguistic Boundaries. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 487–499, Rabat, Morocco. Association for Computational Linguistics.