"Auto-Captions Aren't Good Enough"
"We've been told auto captions aren't acceptable. But manually editing 200 lecture videos will take months."
Illustrative faculty concern, not a customer testimonial
Auto-captions can contain terminology, timing, and speaker errors. Use a representative sample to measure cleanup and human review time before planning a larger rollout.
Measure
Manual caption editing time on your videos
Measure
AI-assisted cleanup plus human review on your videos
Why Auto-Captions Fail WCAG Compliance
1. Technical Terms Mangled
Auto-captions don't know discipline-specific vocabulary. Results are gibberish.
Auto-Caption
"the mitochondria is responsible for 80 p production"
Should Be
"the mitochondria is responsible for ATP production"
2. Missing Punctuation
Auto-captions run sentences together. Screen readers can't parse meaning.
Auto-Caption
"the results were significant however we need more data to confirm our hypothesis lets look at the next slide"
Should Be
"The results were significant. However, we need more data to confirm our hypothesis. Let's look at the next slide."
3. Names and Acronyms Wrong
Proper nouns, researcher names, and acronyms are frequently incorrect.
• "nietzsche" "neat chick"
• "WCAG" "weak egg"
• "Foucault" "fu co"
• "PyTorch" "pie torch"
4. No Speaker Labels
WCAG 1.2.2 requires identifying speakers in multi-person videos. Auto-captions don't do this.
Auto-Caption
"that's a great point what do you think about X?"
Should Be
Professor Smith: That's a great point.
Student Johnson: What do you think about X?
Manual Caption Editing: The Time Sink
Illustrative manual-work assumptions for a 60-minute lecture video; replace these with measured timings:
1. Download auto-captions from YouTube/Zoom: 5 min
2. Watch video + fix technical terms: 90 min
3. Add punctuation + capitalization: 30 min
4. Add speaker labels (if panel/interview): 45 min
5. Final review + upload: 15 min
Illustrative total: 185 minutes per video
185 minutes × 200 videos = about 617 hours in this illustrative scenario
How to Evaluate Aelira Caption Cleanup
Caption Workflow Acceptance Checks
Confirm Supported Media and Caption Inputs
The public CLI documents video/audio transcription. Confirm support for your media and existing VTT/SRT files in the current deployment before planning caption enhancement.
Agree a Terminology Review List
Prepare technical terms, names, and acronyms for the reviewer. Confirm whether the evaluated workflow can use this context.
Names: Dr. Sarah Johnson, Dr. Michael Chen
Acronyms: WCAG, ADA, NVDA
AI-assisted cleanup draft
Where the evaluated workflow produces a draft, check these editing goals against the recording:
- Check technical terms against the recording
- Check punctuation and capitalization
- Check names and acronyms
- Check speaker identification where needed
Review the Saved Captions
Review timing, speech, meaningful sounds, and speakers against the recording. Correct remaining issues and verify the exported file before publication.
Caption Editing Goals to Validate
- Technical terminology (discipline-specific)
- Punctuation (periods, commas, question marks)
- Capitalization (proper nouns, start of sentences)
- Names and acronyms (from context list)
- Speaker identification (multi-person videos)
Time Savings
Manual Editing
Measure on your sample
With Aelira
Measure in pilot
Compare transcription, caption editing, and review time on representative videos.
Illustrative Biology Caption Editing Example
Illustrative Caption Errors
Unintelligible to students relying on captions
Illustrative Corrected Draft
Example cleanup draft; a reviewer must check accuracy, timing, speakers, and terminology
Illustrative Editing Tasks (60-min video)
Manual Editing
- Watch + fix terms: 90 min
- Add punctuation: 30 min
- Review: 15 min
- Total: 135 min
With Aelira
- Upload + supported AI cleanup: measure
- Review saved captions against the recording: measure
- Final corrections and synchronization: measure
- Total: record your pilot result
Measure editing and review time on your own video sample
Manual timings above are illustrative planning assumptions. No measured Aelira saving is established here; a human must review saved captions against the recording.
Stop Spending Hours on Caption Editing
Evaluate supported AI edits and measure the human review and correction effort on representative videos.
Ask about a bounded pilot · Media scope and acceptance criteria agreed in writing
See Caption Cleanup Demo
Evaluate caption cleanup on a representative lecture, including timing, terminology, speakers, and human review.