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

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2601.18899 [cs.CL]

(or arXiv:2601.18899v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.18899 Focus to learn more arXiv-issued DOI via DataCite

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