按语系共享连接器：多语言语音识别新策略 Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries

精读摘要

基于 LLM 的语音识别（ASR）系统通过轻量连接器把冻结的语音编码器接到预训练 LLM 上，以有限资源取得强性能。此前工作每种语言单独训练连接器，忽略了语言之间的亲缘关系。这项研究提出按语系共享连接器的策略——一个语系一个连接器，并在两种多语言 LLM 和两个真实语料库上验证了有效性，为低资源语言 ASR 提供了更经济的方案。 LLM-powered ASR systems link a frozen speech encoder to a pretrained LLM via a lightweight connector, but prior work trains a separate connector per language, overlooking linguistic relatedness. This work proposes connector-sharing by linguistic family, one connector per family, and validates it across two multilingual LLMs and two real-world corpora. It offers a more economical path for low-resource language ASR.