Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026

2026-08-25

AI Summary

本文提出了一种数据增强方法，通过合成语音生成和自动SOAP监督来统一异构医疗对话数据集，以实现端到端的语音到SOAP生成，从而减轻医护人员的工作负担。

Abstract

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.

Problem

• Speech-to-SOAP
• Clinical Notes
• Medical Dialogues
• Data Augmentation

Innovation

• data augmentation pipeline
• synthetic speech generation
• automatically generated SOAP supervision
• end-to-end speech-to-SOAP