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Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries

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Computer Science > Computation and Language

arXiv:2601.18899 (cs)
[Submitted on 26 Jan 2026 (v1), last revised 18 Aug 2026 (this version, v3)]

Title:Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries

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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.
Comments: Accepted by EACL'26 main
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
Cite as: arXiv:2601.18899 [cs.CL]
  (or arXiv:2601.18899v3 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2601.18899
arXiv-issued DOI via DataCite

Submission history

From: Yuchen Zhang Dr [view email]
[v1] Mon, 26 Jan 2026 19:11:03 UTC (112 KB)
[v2] Mon, 2 Feb 2026 18:02:52 UTC (112 KB)
[v3] Tue, 18 Aug 2026 12:13:00 UTC (112 KB)
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