Video localization for universities
Your existing lectures, MOOCs and campus videos, speaking new languages. Dubbed, lip-synced and subtitled at AI speed, then checked line by line by professional translators.
Video localization for universities is the process of converting existing lecture recordings, MOOCs and campus videos into other languages using AI dubbing, lip-sync and subtitling, with professional translators reviewing every line before publication. HumAIn Connection delivers this in more than nine languages for higher education institutions across Europe.
The problem with a video back catalogue
Most universities are sitting on years of recorded teaching that only works in one language. The recordings exist, the teaching is good, and none of it is reachable by the international students the institution spent a decade recruiting.
The traditional route is a language service provider quoting per word for subtitles and per minute for voice work, with turnaround measured in weeks. For a single flagship course that is affordable. For a back catalogue of several hundred hours it is a budget line nobody will approve, so the catalogue stays monolingual and the internationalization target stays on the slide deck.
The other route is running the videos through a consumer AI dubbing tool. That is fast and cheap, and it produces output that mistranslates the terminology your discipline is built on, mangles researcher names, and occasionally invents a sentence. Published under a university logo, that is not a saving. It is a credibility problem in front of the exact audience you were trying to impress.
What we deliver
We sit in the middle of those two routes on purpose: the throughput of an AI pipeline, with a professional linguist accountable for what comes out of it.
- Lip-sync dubbing in 9+ languages
- The speaker can keep their own voice through voice cloning only after the institution confirms the lawful basis and documents the required authorization. The mouth movement is then matched to the new audio so the result does not read as an overdub.
- Subtitles and transcripts
- Timed, segmented for readability rather than for the machine, and delivered in the caption formats your video platform and your accessibility team actually use.
- Human post-editing on every line
- A professional translator working into their native language reviews the full output before it is delivered. This is not a spot check on a sample.
- A terminology glossary that persists
- Course-specific and discipline-specific terms are agreed once and reused across every future video, so the language of your programme stays consistent as the catalogue grows.
How a project runs
Every engagement follows the same four steps, and a human owns the first and the last one.
Listen
We scope with the language centre and the international office: which courses matter, which languages your student population actually needs, what tone the institution uses, and which terminology is non-negotiable.
Build
The AI pipeline handles transcription, translation, voice generation and lip-sync. This is the step that used to take weeks and now takes days, and it is the only step where no human is in the loop.
Review
A professional native translator post-edits the full transcript and the subtitle timing, checks terminology against the glossary, and flags anything the pipeline got confidently wrong.
Care
Course content changes. We update existing localizations rather than asking you to re-buy them, and the glossary carries forward.
What the human review step actually involves
Every localization vendor now claims human review. The phrase has been diluted to the point of meaning nothing, so here is what it concretely covers on our side.
- A professional translator working into their native language, not a bilingual reviewer working into a second language.
- The full output is read, not a sampled percentage of it.
- Discipline terminology is checked against the agreed glossary rather than against the reviewer's general knowledge.
- Named entities are verified: researcher names, institution names, module codes, citations. These are where AI transcription fails most often and most visibly.
- Academic register is corrected. Machine translation tends to flatten a lecture into a customer-service voice, and that reads wrong to students and worse to faculty.
- Subtitle segmentation and reading speed are adjusted for humans reading under time pressure, which is not what the machine optimizes for.
Languages
More than nine languages, with native-speaker review on all delivered output. The deepest benches are Dutch, German, French and English, which reflects where our linguist team actually works rather than where a language list looks impressive.
We will tell you before a project starts whether we have a native reviewer for a given language. If we do not, we say so rather than quietly routing it through a generalist, because the review step is the entire product.
Timeline
| Scoping call and glossary agreement | 1 week |
|---|---|
| Pilot: one representative video, one language | 5 to 7 working days |
| Standard batch, once the glossary is set | Days per video, not weeks |
| Additional languages on an already-processed video | Faster than the first, the pipeline work is done |
What drives the cost
We do not publish a rate card yet, because a number without a scope behind it is noise. What we can tell you is exactly what moves it, so you can size the work before you talk to us.
- Total video minutes, which matters more than the number of videos.
- Number of target languages, where the second and third are cheaper than the first.
- Whether you need dubbing and lip-sync or subtitles alone.
- Turnaround, since compressed deadlines mean more reviewers working in parallel.
- Source quality. Clean audio and an existing transcript reduce the work; a lecture hall recording with a distant microphone increases it.
Every engagement starts with a scoped pilot, priced and agreed in writing before any work begins. No open-ended retainers, and no per-word invoicing that nobody can forecast.
Questions we get asked
Do you dub the video or subtitle it?
Either, or both. Subtitling is cheaper and faster and is usually the right first step for a large back catalogue. Dubbing with lip-sync gives a much better experience for teaching content students actually sit through, so it tends to be worth it on flagship courses and on anything used for recruitment.
Will the dubbed version still sound like the lecturer?
Yes, if the institution approves the lawful basis and governance process for voice cloning and the lecturer is properly informed and authorized under that process. We do not treat an employee signature alone as automatic proof of valid GDPR consent. If voice cloning is not approved, we use a neutral synthetic voice instead.
How do you handle subject-specific terminology?
We agree a glossary with your subject staff before the first video is processed, and the reviewer checks the output against it rather than against general usage. The glossary persists across the whole engagement, which is what keeps terminology consistent as more videos are added over time.
How long does a 60-minute lecture take?
Once the glossary is agreed, a first language typically comes back within about a week, and that time is mostly the human review rather than the AI processing. Additional languages on the same video are faster because the transcription and segmentation work is already done.
What do you need from us to start?
The video files, any existing transcripts or slide decks, a list of terms that must be translated a specific way, and a named contact who can approve terminology decisions. Existing transcripts and slides measurably improve the output, so it is worth digging them out.
What happens if the output is wrong?
You tell us and we fix it, and the correction goes into the glossary so the same error does not recur in the next video. A vendor whose product is human accountability does not get to treat corrections as change requests.
Related services
Start with one pilot, and judge the review.
Tell us what you are trying to do and we will tell you honestly whether we are the right people for it.
Prefer email? hugo.megardon@megaaisolutions.com