构建合成医患对话数据集，用于长时音频摘要任务。
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
- 用角色驱动生成对话，模拟真实医患交流
- 生成8800段对话，含1300小时音频与参考病历
- 适合研究长序列语音理解与医疗自动化系统
长时音频推理在训练数据和评估方面均严重不足。现有基准多针对短上下文任务，而最相关的开放式生成任务因自动评估困难难以开展。本文提出一种合成数据生成流程，既可作为训练资源，也可作为受控评估环境，并应用于首次就诊医患对话，以生成SOAP病历为任务目标。该流程包含三个阶段：基于角色的对话生成、带重叠/停顿建模的多人语音合成及室内外声学与音效模拟、基于大模型的参考SOAP病历生成，全部基于开源权重模型构建。我们发布了8800段合成对话，对应1300小时音频和参考病历。评估当前开源模型发现，级联式方法仍显著优于端到端模型。
原文摘要 · Abstract (English)
Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open-ended generation tasks most relevant to long-context reasoning pose well-known challenges for automatic evaluation. We propose a synthetic data generation pipeline designed to serve both as a training resource and as a controlled evaluation environment, and instantiate it for first-visit doctor-patient conversations with SOAP note generation as the task. The pipeline has three stages, persona-driven dialogue generation, multi-speaker audio synthesis with overlap/pause modeling, room acoustics, and sound events, and LLM-based reference SOAP note production, built entirely on open-weight models. We release 8,800 synthetic conversations with 1.3k hours of corresponding audio and reference notes. Evaluating current open-weight systems, we find that cascaded approaches still substantially outperform end-to-end models.
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