What's the minimum acceptable accuracy for auto captions?

For most content, 93%+ accuracy is acceptable. Below 90%, viewers notice errors frequently enough to be distracted. If your content type consistently generates below 90% accuracy, manual editing is necessary.

Do caption errors affect algorithm performance?

Not directly — algorithms don't read your burned-in captions. But errors reduce watch-through rate and engagement, which the algorithm does measure. So errors affect performance indirectly.

Can I build a custom dictionary for auto captions?

Some tools support custom vocabularies that improve accuracy for specific terms. If your content uses recurring names, brands, or technical terms, this is the single best investment for caption quality — it gets you to 97%+ accuracy without manual editing.

Should I include filler words in captions?

Generally no. Removing "um," "uh," "like," and "you know" from captions makes them easier to read without changing the meaning. Most auto-caption tools inconsistently capture fillers anyway.

How do I handle multiple speakers in captions?

For short clips (under 90 seconds), speaker labels usually aren't necessary — the video shows who's talking. For longer clips or audio-only content, add speaker labels ("Host:" / "Guest:") to the first line of each speaker change.

Are auto captions good enough for accessibility compliance?

For most social media, auto captions meet the spirit of accessibility even with minor errors. For formal accessibility compliance (WCAG), you need 99%+ accuracy, which requires manual review. If accessibility is a legal requirement, always use manual captions.

Auto Captions vs Manual Captions: Which Drive More Engagement?

Auto Captions vs Manual Captions: Which Drive More Engagement? — Dubcap Blog