{"slug": "ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in", "title": "AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Workflow for English-Script Rendering and Semantic Drift in Korean-English Medical Lectures", "summary": "A retrospective pilot study of AI_LectureNote, a post-ASR workflow for Korean-English medical lectures, found that while the system improved English-script rendering rates from 0.39 to 0.71 on whisper-1 and from 0.26 to 0.65 on gpt-4o-transcribe, it introduced semantic drift in 34 and 36 of 282 reference sentences and polarity failures in 11 and 13 of 101 polarity-cue rows across two post-processed conditions. The single-annotator study by the authors documents concrete failure modes and supports evaluating surface accuracy, term-script rendering, chunk-level script consistency, and medical-meaning preservation separately.", "body_md": "arXiv:2607.17237v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in", "canonical_source": "https://www.machinebrief.com/news/ailecturenote-a-retrospective-pilot-study-of-a-post-asr-work-pulj", "published_at": "2026-07-21 04:00:00+00:00", "updated_at": "2026-07-21 04:37:28.123085+00:00", "lang": "en", "topics": ["artificial-intelligence", "natural-language-processing", "ai-research", "machine-learning"], "entities": ["AI_LectureNote", "whisper-1", "gpt-4o-transcribe"], "alternates": {"html": "https://wpnews.pro/news/ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in", "markdown": "https://wpnews.pro/news/ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in.md", "text": "https://wpnews.pro/news/ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in.txt", "jsonld": "https://wpnews.pro/news/ai-lecturenote-a-retrospective-pilot-study-of-a-post-asr-workflow-for-english-in.jsonld"}}