ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark
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2026
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| author | Nguyen, Tung X. Vo, Nhu Nguyen, Giang-Son Hoang, Duy Mai Huynh, Chien Dinh Unanue, Inigo Jauregi Piccardi, Massimo Buntine, Wray Le, Dung D. |
| author_facet | Nguyen, Tung X. Vo, Nhu Nguyen, Giang-Son Hoang, Duy Mai Huynh, Chien Dinh Unanue, Inigo Jauregi Piccardi, Massimo Buntine, Wray Le, Dung D. |
| contents | 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 \textbf{Vi}etnamese \textbf{Med}ical \textbf{C}ode-\textbf{S}witching \textbf{S}peech 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12911 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark Nguyen, Tung X. Vo, Nhu Nguyen, Giang-Son Hoang, Duy Mai Huynh, Chien Dinh Unanue, Inigo Jauregi Piccardi, Massimo Buntine, Wray Le, Dung D. Computation and Language 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 \textbf{Vi}etnamese \textbf{Med}ical \textbf{C}ode-\textbf{S}witching \textbf{S}peech 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. |
| title | ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.12911 |