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.

翻译：基于大型语言模型（LLM）的自动语音识别（ASR）系统通过轻量级连接器将冻结的语音编码器与预训练LLM相连接，从而在有限资源下实现强劲性能。先前的研究为每种语言单独训练连接器，忽略了语言间的亲缘关系。我们提出了一种基于语言家族隶属关系的高效新颖的连接器共享策略，实现每个语言家族共享一个连接器，并基于两种多语言LLM和两个涵盖精选与众包语音的真实世界语料库，实证验证了该策略的有效性。我们的结果表明，基于家族的连接器在减少参数数量的同时，提升了跨领域的泛化能力，为多语言ASR部署提供了一种实用且可扩展的策略。