AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Workflow for English-Script Rendering and Semantic Drift in Korean-English Medical Lectures
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 s
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- FuzzySimilar title/name (fuzzy) · 87%fengshao1227/ccg-workflow →
“Fuzzy title match (0.94): “AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Wo” ≈ “fengshao1227/ccg-workflow””
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
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- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Wo” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Kyeongeon Lee →
“AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Workflow for English-Script Rendering and Semantic Drift in Ko”
- LinkedLinked via arxiv author · 85%Donghoon Chang →
“AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Workflow for English-Script Rendering and Semantic Drift in Ko”