发表机构
Karlsruhe Institute of Technology; Carnegie Mellon University（卡尔斯鲁厄理工学院; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对医学对话摘要任务，提出KIT团队参加BeTraC挑战赛轻量级赛道的方案，构建可扩展数据增强流水线，适配语音基础模型实现端到端语音转SOAP生成，减轻医护记录负担。
AI 中文摘要
随着大语言模型及其指令遵循能力的出现，摘要生成成为一项极具前景的应用任务。在该任务领域中，临床记录的抽取式子任务已成为特别受关注的主题，因为它能显著减少医护人员的停机时间和记录负担，使他们能够专注于帮助患者的核心工作。迈向自动化的又一步是直接从语音生成临床记录，无需中间转录文本，这既能减少处理时间，又能保留咳嗽等副语言线索，而这些线索在基于转录文本的系统中可能会丢失。为此，我们提交了KIT团队参加今年BeTraC挑战赛轻量级赛道的方案。我们的主要贡献是一个可扩展的数据增强流水线，它通过合成语音生成和自动生成的SOAP监督，统一了异构医学对话数据集，从而实现了语音基础模型对端到端语音到SOAP生成的稳健适配。
英文摘要
With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within this domain of task the extractive sub-task of clinical protocolling has emerged as a topic of particular interest as it can significantly reduce the downtime and protocolling burden of health-care workers thus enabling them to focus on their core work helping humans. A further step towards automation is the direct generation of clinical notes from speech without intermediate transcripts, reducing processing time while preserving information such as coughing or other paralinguistic cues that may be lost in transcript-based systems. To this end, we present KIT's submission to this years BeTraC challenge in the lightweight track. Our main contribution is a scalable data augmentation pipeline that unifies heterogeneous medical dialogue datasets through synthetic speech generation and automatically generated SOAP supervision, enabling robust adaptation of a speech foundation model for end-to-end speech-to-SOAP generation.
Comments3 pages, BeTraC 2026
