The problem it tries to solve
Automatic captioning tools learn from large amounts of everyday speech. Common words are easy for them; unusual ones are not. A surname, a place name, a company term, a drug name or a piece of technical slang may have appeared rarely or never in the material the system learned from. When the software hears such a word, it often substitutes the closest common word or phrase. A speaker mentioning a specialist term can turn into a few ordinary words that sound similar and mean something entirely different.
A custom vocabulary is one way to push back. Many tools let you supply a list of words and phrases you expect in the recording, so the software treats them as more likely when it chooses between candidates.
How it generally works
The details differ from tool to tool, and some tools do not offer the feature at all. In general, a vocabulary list nudges the recognition step toward certain words. Because the software is choosing between several plausible readings of a sound, giving a listed term extra weight can tip the choice toward the correct one.
Some tools simply accept a plain list. Others let you attach extra information, such as a spoken form or a boost level indicating how strongly to favor a term. Some offer a related feature that replaces one output wording with another after recognition, which can fix consistent mistakes in spelling or capitalization but cannot recover a word the software never recognized. Check what your tool actually does rather than assuming.
What kinds of terms benefit
A list tends to help most with:
- Personal and place names that are spelled in a particular way, especially names that are uncommon in the language of the recording.
- Organization and product names used repeatedly in the material.
- Technical terms and abbreviations from a field, such as medical terminology, legal vocabulary or engineering shorthand.
- Recurring in-house words, such as the name of a program, a team or an event.
It helps less with common words that the software already knows, and it is usually not worth listing hundreds of ordinary words.
Building a good list
A useful list is short, specific and based on the actual material. Some practical steps:
- Start from the source. Look at the script, slides, agenda, speaker list or earlier transcripts, and collect the terms that matter.
- Prioritize repeat offenders. Run a first pass without a list, note which words are wrong more than once, and add those first.
- Use the correct spelling and capitalization you want in the output, and decide on a consistent style for abbreviations.
- Include variants if needed. If a name is said in different ways, such as a short form and a full form, a tool may need both.
- Keep it focused. Overloading a list can backfire, because a very large boost on many terms may cause the software to insert them where they were not spoken.
- Re-test. After adding the list, run the same clip again and compare. Keep only the terms that clearly help.
This work overlaps with building a term glossary for a subtitle project. A glossary is a human-facing reference of approved spellings and translations, while a custom vocabulary is the machine-facing version of part of it. Starting from one list and adapting it for both purposes saves effort, and it supports the routine of checking names and terms before captions are published.
Limits to keep in mind
It is not a guarantee. A listed term may still be missed or misheard, especially if the audio is poor. A custom vocabulary improves the odds; it does not remove errors. Recording conditions still matter a great deal, and poor audio quality can overwhelm any list.
It can create false positives. If a listed word sounds like a common word, the software may sometimes choose the listed one when the speaker said the common one. Review the output for this kind of mistake after you add a term.
It does not teach the software pronunciation by itself. Some tools handle unusual pronunciations better than others. A name pronounced differently from how it looks may need extra information, or may simply need correction by hand.
It rarely helps with accents on its own. If a speaker’s pronunciation is the problem, a word list may help only a little. That is a separate issue from missing vocabulary.
Privacy and sharing. Word lists often contain names of real people or internal project terms. Consider what you are uploading and where it goes, in the same way you would consider the audio itself.
Where it fits in the workflow
Vocabulary lists are a preparation step, done before transcription, and they reduce the number of corrections needed afterward. They do not replace review. After the machine pass, a person should still read through the draft, paying special attention to proper names, numbers and any term whose error would change meaning. For specialist or sensitive material, a reviewer who knows the subject is far better placed to catch subtle errors than any word list.
A reasonable expectation is a modest but real gain: fewer repeated mistakes on the words you care about most, and less tedious find-and-replace afterward. Treat the list as a living document, update it as new projects bring new terms, and drop entries that stop earning their place.