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arXiv · 2602.12911

ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark

Tung X. Nguyen·Nhu Vo·Giang-Son Nguyen·Duy Mai Hoang·Chien Dinh Huynh·Inigo Jauregi Unanue·Massimo Piccardi·Wray Buntine·Dung D. Le
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Abstract

Code-switching (CS), which is when Vietnamese speech uses English words like drug names or procedures, is a common phenomenon in Vietnamese medical communication. This creates challenges for Automatic Speech Recognition (ASR) systems, especially in low-resource languages like Vietnamese. Current most ASR systems struggle to recognize correctly English medical terms within Vietnamese sentences, and no benchmark addresses this challenge. In this paper, we construct a 34-hour Vietnamese Medical Code-Switching Speech dataset (ViMedCSS) containing 16,576 utterances. Each utterance includes at least one English medical term drawn from a curated bilingual lexicon covering five medical topics. Using this dataset, we evaluate several state-of-the-art ASR models and examine different specific fine-tuning strategies for improving medical term recognition to investigate the best approach to solve in the dataset. Experimental results show that Vietnamese-optimized models perform better on general segments, while multilingual pretraining helps capture English insertions. The combination of both approaches yields the best balance between overall and code-switched accuracy. This work provides the first benchmark for Vietnamese medical code-switching and offers insights into effective domain adaptation for low-resource, multilingual ASR systems.

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Tung X. Nguyen, Nhu Vo, Giang-Son Nguyen, Duy Mai Hoang, Chien Dinh Huynh, Inigo Jauregi Unanue, Massimo Piccardi, Wray Buntine, Dung D. Le. 2026-02-13. ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark. https://doi.org/10.63317/58uwrquo3znb

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