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

arXiv: https://arxiv.org/abs/2602.12911 · HTML: https://mlsanity.com/paper/2602.12911/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR](https://mlsanity.com/paper/2406.06619/) (2024)
- [PhoWhisper: Automatic Speech Recognition for Vietnamese](https://mlsanity.com/paper/2406.02555/) (2024)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [Attention-Guided Adaptation for Code-Switching Speech Recognition](https://mlsanity.com/paper/2312.08856/) (2023)
- [VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain](https://mlsanity.com/paper/2404.05659/) (2024)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding](https://mlsanity.com/paper/2412.16507/) (2024)
- [Improving Vietnamese-English Medical Machine Translation](https://mlsanity.com/paper/2403.19161/) (2024)
- [OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary](https://mlsanity.com/paper/2506.09448/) (2025)
- [VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining](https://mlsanity.com/paper/2505.21527/) (2025)
- [AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR](https://mlsanity.com/paper/2501.07102/) (2025)

## Built on by (most cited first, 3 of 3)

- [Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition](https://mlsanity.com/paper/2606.06985/) (2026)
- [PiDA: Phonetically-Informed Data Augmentation for Robust Vietnamese Speech Translation](https://mlsanity.com/paper/2606.12911/) (2026)
- [VietPrism: A large-scale Vietnamese speech and deepfake corpus with diverse dialects and code-switching](https://mlsanity.com/paper/2609.30005/) (2026)

## Abstract (arXiv)

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.