发表机构
Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对韩英医学讲座后ASR工作流程AI_LectureNote进行回顾性试点，通过恢复拉丁字母术语重写语音转文本输出，评估不同条件下脚本渲染率及语义忠实度，揭示具体失败模式，支持多方面单独评估。
AI 中文摘要
AI_LectureNote是一个面向可读性的韩英医学讲座后ASR工作流程。它将语音转文本输出重写为学习成绩单，同时恢复拉丁字母医学术语而非韩语语音转写。我们在五个条件下对四场作者录制的讲座进行回顾性评估。在这个试点中，后处理将whisper-1路径上的宏观英文脚本渲染率从0.39提高到0.71，应用于3分钟分块的gpt-4o转录输出时从0.26提高到0.65。然而，英文脚本渲染并不意味着语义忠实：两个后处理条件在282个参考句子中有34个和36个出现语义漂移，在101个极性提示行中有11个和13个出现极性失败。描述性跨输入比较表明了不同的候选失败模式。这个单注释试点记录了具体的失败模式而非总体发生率，并支持分别评估表面准确性、术语脚本渲染、块级脚本一致性和医学意义保留。
英文摘要
AI_LectureNote is a historical, readability-oriented post-ASR workflow for Korean-English medical lectures. It rewrites speech-to-text output into study transcripts while restoring Latin-script medical terms rather than Korean phonetic transliterations. We retrospectively evaluate the workflow on four author-recorded lectures across five conditions. In this pilot, post-processing raised the macro English-script rendering rate from 0.39 to 0.71 on the whisper-1 path and from 0.26 to 0.65 when applied to 3-minute chunked gpt-4o-transcribe output. However, English-script rendering did not imply semantic faithfulness: the two post-processed conditions showed semantic drift in 34 and 36 of 282 reference sentences and polarity failures in 11 and 13 of 101 polarity-cue rows. A descriptive cross-input comparison suggested different candidate failure patterns: polarity-failure sets overlapped more strongly across front-ends (Jaccard 0.60; 9 shared of 15 unioned failures) than general semantic-drift sets (Jaccard 0.23; 13 shared of 57 unioned drifts). This single-annotator pilot documents concrete failure modes rather than population rates and supports evaluating surface accuracy, term-script rendering, chunk-level script consistency, and medical-meaning preservation separately.
Comments12 pages, 4 figures
