测试语音转写在瑞士居家护理中的应用效果
Local Transcription Models in Home Care Nursing in Switzerland: an Interdisciplinary Case Study
- 用OpenAI Whisper模型测试德语方言和口音的转写能力
- 无需微调的默认模型已达到可用水平
- 适合医疗NLP研究者和护理信息化从业者
自然语言处理的最新进展为医疗领域带来新应用，如语音转写可辅助护理记录自动化，提升护士与患者互动时间。但面临数据隐私、本地语言方言及专业术语等挑战。本案例研究聚焦瑞士居家护理记录场景，评估多种转写工具与模型，对OpenAI Whisper进行多组实验，涵盖德语方言、外语口音及护理专家手动整理的语料。结果表明，即使不经过微调的原生模型也具备足够性能，可作为该领域未来研究的良好起点。
原文摘要 · Abstract (English)
Latest advances in the field of natural language processing (NLP) enable new use cases for different domains, including the medical sector. In particular, transcription can be used to support automation in the nursing documentation process and give nurses more time to interact with the patients. However, different challenges including (a) data privacy, (b) local languages and dialects, and (c) domain-specific vocabulary need to be addressed. In this case study, we investigate the case of home care nursing documentation in Switzerland. We assessed different transcription tools and models, and conducted several experiments with OpenAI Whisper, involving different variations of German (i.e., dialects, foreign accent) and manually curated example texts by a domain expert of home care nursing. Our results indicate that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.
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