You are on lesson 5 of 11 in the course Path 1: Captions.
Module 1.2: Common Caption Errors and How to Fix Them
Even the best automated captioning systems need human review. While AI-powered captions typically achieve 95%+ accuracy, that remaining percentage matters for the people relying on them. A misspelled council member's name or incorrect budget figure affects how well community members can follow and participate in their government.
This article explains the most common caption errors in government meetings and practical techniques for fixing them efficiently using editing tools like MediaScribe's Caption Editor.
Why caption accuracy matters
WCAG Success Criterion 1.2.2 establishes standards for caption quality to ensure "effective communication." When captions show "Mayor Smith" as "Mary Smith" or "$1.5 million" as "$50,000," people who are deaf or hard of hearing receive different information than hearing attendees. Accurate captions mean everyone gets the same information, regardless of how they access meetings.
Most caption errors fall into predictable categories that are easy to fix once you know what to look for.
The five most common caption error types
1. Proper nouns and names
The problem: Speech recognition struggles with names not in standard vocabularies. Council members, streets, and organizations often appear misspelled or phonetically incorrect.
Before:
Good evening. I'm Council Member Garcia. Tonight we're discussing the Mason Street rezoning proposal.
After:
Good evening. I'm Council Member Garcia. Tonight we're discussing the Mayson Street rezoning proposal.
How to fix: Create a custom vocabulary of frequently mentioned names. Add council members, department heads, streets, and landmarks. Use Find & Replace to correct recurring names throughout transcripts.
2. Numbers and financial figures
The problem: Spoken numbers are easily misheard. "Fifteen" becomes "fifty." Large figures get extra zeros or decimal points in the wrong place.
Before:
The total project cost is $15,000,000 with $3,000,000 allocated to phase one.
After:
The total project cost is $1,500,000 with $300,000 allocated to phase one.
How to fix: Verify all numbers against official documents like agendas or budget reports. Always double-check figures that determine funding, deadlines, or measurements.
3. Technical terminology and acronyms
The problem: Government work involves specialized vocabulary. Generic speech recognition guesses at unfamiliar zoning codes, permit types, and acronyms—often incorrectly.
Before:
We need to update the CUP for that property before the EIR can be completed.
After:
We need to update the Conditional Use Permit for that property before the Environmental Impact Report can be completed.
How to fix: Decide on consistent acronym handling. Spell out on first use ("Conditional Use Permit (CUP)") or ensure accurate abbreviations. Add technical terms to your custom vocabulary and create a glossary for editors.
4. Speaker identification
The problem: Captions without speaker labels confuse viewers. Caption readers can't tell who said what without visual or voice cues.
Before:
I agree with that assessment.
Well, I don't. I think we need more data.
Can we table this until next month?
After:
COUNCIL MEMBER JOHNSON: I agree with that assessment.
COUNCIL MEMBER MARTINEZ: Well, I don't. I think we need more data.
MAYOR THOMPSON: Can we table this until next month?
How to fix: Add speaker labels at the start of each statement. Format consistently: all caps for name, colon, then statement. Create speaker profiles in your caption system for automatic voice identification.
5. Non-speech sounds
The problem: Captions should include important non-speech audio like applause or gavels—sounds that provide context.
Before:
That concludes our discussion. We'll move to a vote.
The motion passes unanimously.
After:
That concludes our discussion. We'll move to a vote.
[applause]
The motion passes unanimously.
How to fix: Use square brackets for meaningful non-speech sounds: [applause], [laughter], [gavel bangs]. Only caption sounds that add context—not every cough or paper shuffle.
Practical editing techniques
Most caption editing systems, including MediaScribe, provide tools designed for efficient error correction. Here's how to work systematically through caption files:
Using Find & Replace effectively
For errors that repeat throughout a transcript—like a misspelled name or consistently wrong term—Find & Replace tools save significant time.
How it works:
Search for the incorrect text (example: "Mary Smith")
Enter the correct version ("Mayor Smith")
Replace all occurrences at once
Before using Replace All, verify:
Does this replacement work in every instance?
Will it create new errors in different contexts?
For example, replacing "he" with "she" throughout a transcript creates problems where "he" is correct. Use Replace All for proper nouns and unique phrases where the replacement is always appropriate.
Editing with audio context
Always verify edits against the actual audio. Caption editors typically provide synchronized playback so you can:
Click a caption segment to jump to that moment in the video
Hear what was actually said
Confirm your correction matches the speaker's words
This prevents "fixing" captions that were actually correct, just unexpected.
Speaker label formatting
Be consistent with speaker identification throughout your file:
Use all caps for speaker names: MAYOR THOMPSON
Follow with a colon: MAYOR THOMPSON:
Then the spoken text: MAYOR THOMPSON: I call this meeting to order.
Consistent formatting helps caption readers quickly identify who's speaking without confusion.
Quality review workflow
Use a systematic review process:
First pass: Scan for names of people, places, and organizations. Verify spelling against official sources.
Second pass: Cross-check statistics, budget figures, and specialized terminology against source documents.
Third pass: Add or correct speaker labels with consistent formatting.
Fourth pass: Watch video portions (sampling different speakers) while reading captions.
Final check: Look for moments needing sound indicators for context.
We've put together a handy Caption Accuracy checklist that can be downloaded/printed here.
Common mistakes to avoid
Don't rely on spell-check alone. It won't catch correctly spelled wrong words—like "Mayor Smith" appearing as "Mary Smith."
Don't skip verification for "close enough" errors. "50th Street" versus "15th Street" directs people to completely different locations.
Don't forget archived meetings. Quality requirements apply to your entire video library.
Don't ignore timing issues. Captions that lag or appear too quickly undermine accessibility.
When to re-caption vs. edit
Edit when overall accuracy is high (90%+), audio quality was good, and errors are predictable and searchable.
Consider re-generation when multiple speakers are consistently misidentified, audio quality was poor, or more than 25% of the transcript contains errors.
Even with re-generation, you'll still need human review.
MediaScribe integration
Quality caption editing serves your whole community—not just meeting compliance requirements. MediaScribe's Caption Editor gives you the tools to ensure everyone receives accurate, accessible information.
Caption Editor features:
Synchronized video playback with caption timeline for context-aware editing
Click-to-edit any caption segment with automatic audio preview
Find & Replace tool for batch corrections of recurring errors
Speaker label assignment and correction
One-click save that automatically updates all caption file formats (SRT, VTT, etc.)
To access the Caption Editor: Navigate to Files → Assets Library, select your recording, and click the pencil icon next to your language track. All edits sync automatically with your video file and update across all distribution formats.
Summary
Key takeaways for caption error correction:
The five most common errors are proper nouns, numbers, technical terms, speaker identification, and non-speech sounds
Create custom vocabularies with frequently mentioned names, streets, and technical terms to prevent recurring errors
Use Find & Replace for batch corrections but verify replacements won't create new problems in different contexts
Follow a systematic multi-pass review workflow: names first, then numbers, then speakers, then overall accuracy
Verify all financial figures and statistics against official documents to ensure community members receive accurate information
Edit existing captions when accuracy is high (90%+); consider re-generation when more than 25% contains errors
Download and use the Caption Accuracy Self-Audit Checklist to ensure your captions meet WCAG 2.1 Level AA standards before publishing
Related WCAG 2.1 criteria
1.2.2 Captions (Prerecorded) [Level A]
1.2.4 Captions (Live) [Level AA]