Can LLM Agents Identify Spoken Dialects like a Linguist?

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Abstract:Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability of large language models (LLMs) as agents in understanding the dialects and whether they can show comparable performance to models such as HuBERT in dialect classification. In addition, we provide an LLM baseline and a human linguist one. Our approach uses phonetic transcriptions produced by ASR systems and combines them with linguistic resources such as dialect feature maps, vowel history, and rules. Our findings indicate that, when linguistic information is provided, the LLM predictions improve. The human baseline shows that automatically generated transcriptions can be beneficial for such classifications, but also presents opportunities for improvement.

Comments: Accepted to DialRes Workshop @ LREC 2026

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2603.29541 [cs.CL]

(or arXiv:2603.29541v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.29541

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Akbar Karimi [view email] [v1] Tue, 31 Mar 2026 10:24:20 UTC (960 KB)