21 August 2026
A transcription tool doesn't hear "titrate" and produce garbage — it hears "titrate" and confidently writes "tight rate," because that's the more common phrase in its training data. This is a substitution problem, not a noise problem, and it's why proofreading a transcript rarely catches it: the sentence still reads fine, just wrong. Fixing it takes custom vocabulary, context about what the meeting is actually about, and a way to correct a term once instead of every time it comes up.
Run a bad recording through any speech model and you'll get obvious garbage — half a word, a string of nonsense syllables. That's easy to spot and easy to ignore. Jargon failure looks nothing like that. The model hears the right sounds and picks the wrong word, and the wrong word is grammatical, plausible, and sitting right where the real word should be. "We titrated the dose" becomes "we tight-rated the dose." Nobody reading that back stops, because the sentence still parses.
This is the part that makes it expensive rather than just annoying. A dropped word gets flagged because the sentence breaks. A substituted word doesn't break anything — it just quietly changes what the record says a team decided, and the person checking it has to already know the right term to notice it's missing.
Speech recognition isn't one step, it's two: an acoustic model maps sound to phonemes, then a language model decides which real words those phonemes probably spell, based on what's statistically likely in ordinary speech. That second step is where jargon dies. A model trained mostly on call-center audio, podcasts and news broadcasts has seen "tight rate" and "tight race" thousands of times more often than "titrate," so when the phonemes are ambiguous, it bets on the word it's seen more.
A biotech team saying "titrate," "efficacy," or a specific compound name a hundred times a week is, from the model's point of view, a rounding error against everyone else in its training data saying "tight rate" or "efficiency." The model isn't confused. It's doing exactly what it was built to do — pick the statistically likely word — and the likely word is wrong for this specific room.
The fix that works is narrowing the model's guess before it guesses, and the cheapest signal for that is what the meeting is already telling you. A meeting titled "Phase 2 PK/PD Review" or "Indemnification Clause — Redline" is a strong hint that the next hour of audio will contain pharmacokinetics or contract law terms, not sports scores. Feeding that title, plus whatever was said in the last ten minutes, back into the recognition step biases the language model's guesses toward the vocabulary of this specific conversation instead of speech in general.
This is different from a static glossary, because it adapts as the meeting moves. A call that opens on dosing and shifts to manufacturing timelines shouldn't keep guessing pharmacology words once everyone's talking about supply chains — recent context should update the bias, not just the meeting title fixed at the start.
A team-level list of terms — drug names, legal terms of art, internal system names, acronyms specific to the project — gives the language model explicit candidates it wouldn't otherwise weight highly. This closes most of the gap for terms that recur constantly: dosing units, product codenames, clause references, part numbers. It's the single highest-leverage fix because it doesn't depend on context clues; the term is just always a live option.
It doesn't fix everything. A brand-new compound name said for the first time in a meeting still has to be caught and corrected, because no vocabulary list can contain a word nobody has told it about yet. Custom vocabulary handles the hundred terms your team says constantly. It won't handle the one your team says for the first time this afternoon.
The step most tools get wrong is what happens after someone notices a mistake. If correcting "tight rate" to "titrate" only fixes that one instance in that one sentence, the same team will retype the same correction next week, and the week after, forever. The correction should do two things: fix every occurrence of that term already in the current transcript, and add the term to the team's vocabulary so it's recognized correctly the next time anyone says it, in any meeting.
AVAY does this for the terms a team adds to its glossary — correct "titrate" once and it's treated as a known term going forward, including in search across past meetings. It won't retroactively fix meetings recorded before the term was added; a correction made today doesn't reach back into last month's calls. That's a real limit, not a rounding error, and it's worth knowing before you assume an old transcript has already been cleaned up.
| What it catches | What it misses | |
|---|---|---|
| General-purpose recognition | Everyday speech, filler, common phrasing | Rare or domain-specific terms — guesses the common word instead |
| Custom vocabulary list | Terms your team says constantly: names, units, acronyms | A term said for the first time — nothing to match against yet |
| Context from title and recent speech | Shifts guesses toward the topic of this specific call | Doesn't help if the title is generic ("Sync," "Catch-up") |
| Manual correction, done once | Fixes the term everywhere in this transcript and going forward | Doesn't reach back into transcripts recorded before the fix |
Because the second stage of speech recognition — the language model — picks the word that's statistically most likely given everything it was trained on, and rare domain terms lose that contest to common words that sound similar. It's not hearing wrong so much as guessing wrong, confidently, in a way that still reads as a normal sentence.
It fixes the terms your team says repeatedly, because the model has an explicit candidate to weigh instead of guessing from general speech patterns. It won't help with a term used for the first time in a given meeting, since there's nothing in the list yet for it to match.
It should fix every instance already in that transcript and add the term so future meetings recognize it correctly on the first try. What it typically won't do is reach backward into meetings recorded before the correction existed — those stay as they were transcribed.
Yes, a specific title ("Phase 2 PK/PD Review" rather than "Sync") gives the recognizer a signal about what vocabulary is likely, which shifts its guesses toward domain terms instead of the most common everyday word. It's not a substitute for a vocabulary list, but it helps most in the opening minutes before recent speech has given the model anything else to go on.
In one specific way, yes: a missing transcript is obviously missing, but a confidently wrong one looks correct and gets trusted. If a team relies on the transcript for what was decided, a silent substitution can misstate a dose, a clause, or a spec without anyone noticing until it matters.
Jargon errors in a transcript aren't noise, they're the model confidently picking the common word over the rare one — the fix is a vocabulary list for the terms your team repeats, context from the meeting itself, and a correction that only has to happen once.
AVAY is a video meeting platform that transcribes the call itself — no bot joins, because there is nothing to join. Start one at avay.ai, read how each part works in the documentation, or see what it costs.