Multilingual Domain Adaptation for Speech Recognition Using LLMs

Date

2026-01-01

Author

Ulu, Elif Nehir
Derya, Ece
Tumer, Duygu
Demirel, Berkan
Karamanlıoğlu, Alper

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We present a practical pipeline for multilingual domain adaptation in automatic speech recognition (ASR) that combines the Whisper model with large language models (LLMs). Using Aya-23-8B, Common Voice transcripts in 22 languages are automatically classified into the Law and Healthcare domains, producing high-quality domain labels at a fraction of the manual cost. These labels drive parameterefficient (LoRA) fine-tuning of Whisper and deliver consistent relative Word Error Rate (WER) reductions of up to 14.3% for languages that contribute at least 800 in-domain utterances. A data-volume analysis reveals a clear breakpoint: gains become reliably large once that 800-utterance threshold is crossed, while monolingual tuning still rescues performance in truly low-resource settings. The workflow therefore shifts the key success factor from expensive hand labelling to scalable data acquisition, and can be replicated in new domains with minimal human intervention.

URI

https://hdl.handle.net/11511/117816

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Department of Computer Engineering, Conference / Seminar