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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.18899 |
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| _version_ | 1866914300272050176 |
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| author | Zhang, Yuchen Shekhar, Ravi Mouratidis, Haralambos |
| author_facet | Zhang, Yuchen Shekhar, Ravi Mouratidis, Haralambos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18899 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries Zhang, Yuchen Shekhar, Ravi Mouratidis, Haralambos Computation and Language Artificial Intelligence Sound 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. |
| title | Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries |
| topic | Computation and Language Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2601.18899 |