Question 1

How accurate is AI subtitling really?

Accepted Answer

For clean, single-speaker audio in a language the model actually supports well, you're typically looking at 90 to 95% word accuracy, sometimes better. That number drops fast though once you introduce background noise, overlapping speakers, strong accents or specialized vocabulary the model hasn't seen much of. Sometimes it falls all the way down into the 60 to 80% range which is a real problem if you're not expecting it.
The smart way to treat this is as a strong first draft, nothing more. The review pass that follows should scale with how rough the audio actually is, light touch for easy stuff, much heavier scrutiny for anything that's noisy or hard to parse.

Question 2

Are AI-generated captions good enough for legal compliance?

Accepted Answer

Not on their own, no, not for regulated content. Standards like WCAG, along with the rules under the ADA and FCC, generally require captions to actually be accurate and synchronized, not just technically present on the screen. Raw AI output often has errors that'll fail an accessibility audit even though, on paper, a caption track exists.

A light human QA pass on the AI draft is what actually closes that gap. Worth saying too, confirm your specific obligations with counsel rather than treating any of this as legal advice.

Question 3

How much cheaper is AI subtitling than manual captioning?

Accepted Answer

The ASR step alone is usually an order of magnitude cheaper than fully manual captioning, cents per minute against dollars per minute, no contest really. But the actual number that matters is total cost, not just that headline rate. AI shifts your spend from transcribing everything from scratch to reviewing and correcting what's already mostly there and that's a fraction of the labour either way you look at it.
For a large back-catalogue, this is honestly what makes full captioning financially possible at all. Without that shift, most operators would just never get to it.

Question 4

What caption file format should I use?

Accepted Answer

For web and HLS streaming, WebVTT (.vtt) is the standard choice. SRT is the most portable for downloads and interchange. TTML/IMSC is common in broadcast and premium VOD where rich styling and positioning matter. Most OTT operators deliver WebVTT as a sidecar track in their HLS or DASH manifest.

Question 5

Can AI handle multiple speakers and identify who is talking?

Accepted Answer

A lot of ASR engines support speaker diarization now, which separates out the different speakers and labels who's talking. It does a decent enough job when people are taking clean turns and not talking over each other, but throw in overlapping speech or two voices that sound similar and it starts falling apart pretty fast.
For closed captions specifically, plan on a human checking and fixing those speaker labels. Getting attribution right isn't a nice-to-have here, it's actually part of what makes the captions genuinely accessible in the first place.

Question 6

Does AI subtitling support translation into other languages?

Accepted Answer

Yes, machine translation can take that transcript and turn it into subtitles in other languages once the transcription's done. Quality swings depending on the language pair and the type of content though and idioms, names, cultural references are pretty much always where things break down first.

If these translated subtitles are actually going out to an audience, budget for a native speaker to review them. And get the order right too, lock your source-language caption first, then translate off that clean transcript rather than translating something that's still got errors sitting in it

Question 7

Will captions actually improve engagement and SEO?

Accepted Answer

Generally yes, it's a bit of a no-brainer once you actually look at what captions do. They let people watch in muted or noisy environments, which covers a huge chunk of social and mobile viewing already. They widen who can actually access your content in the first place. And they hand search and recommendation systems clean, indexable text to work with, which most platforms are otherwise missing entirely.

That's exactly why a lot of operators treat captions as one of the higher-ROI things they can do to their catalogue. One single asset, three different jobs, accessibility, engagement and discovery, all at once.

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