ViMedCSS：越南语医学术语代码转换语音数据集与基准
ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark
AI总结：
针对越南语医学术语代码转换问题，构建了34小时语音数据集ViMedCSS，评估多种ASR模型并探索微调策略，发现越南语优化模型与多语言预训练结合效果最佳。
摘要：
代码转换（CS）是越南语语音中夹杂英语词汇（如药物名称或医疗程序）的常见现象，给自动语音识别（ASR）系统带来挑战，尤其是在越南语等低资源语言中。当前大多数ASR系统难以正确识别越南语句子中的英语医学术语，且缺乏针对此挑战的基准。本文构建了一个34小时的越南语医学术语代码转换语音数据集（ViMedCSS），包含16,576条语句，每条语句至少包含一个来自精心策划的双语词典的英语医学术语，覆盖五个医学主题。利用该数据集，我们评估了多种最先进的ASR模型，并研究了不同的特定微调策略以改进医学术语识别，从而探索解决该数据集的最佳方法。实验结果表明，越南语优化模型在通用片段上表现更好，而多语言预训练有助于捕捉英语插入语。两种方法的结合在整体准确率和代码转换准确率之间取得了最佳平衡。本工作为越南语医学术语代码转换提供了首个基准，并为低资源多语言ASR系统的有效领域适应提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
