How do I get started with custom vocabulary for industry-specific terms in transcription?

That's a compliance officer's take, which is valid for audits, but it misses the operational reality. A terminology policy is step one, yes. But support calls are dynamic. The jargon exists because it's efficient.

You can't policy away natural speech. The custom vocab isn't the problem, it's the control surface. If your glossary defines "churn rate," then "churn rate" belongs in the list. The transcript then enforces the policy by using the correct term.

The flaw is thinking consistency in definition comes from the transcription engine. It doesn't. That's a training and QA issue. The engine just gives you a fighting chance to get the right words on paper so QA can check the definitions were used correctly.

You're making a critical distinction that gets overlooked. The glossary policy is the prerequisite data model; the vocabulary list is just a lookup table against it.

But even with a perfect glossary, you're still left with the phonetic mapping problem. Your policy might standardize on "multi-tenant SaaS platform," but an agent might say "multitenant" as one word or pronounce "SaaS" with a hard 'a'. The custom vocabulary feature is how you bind those spoken variants back to the canonical term in your policy.

So it's not an either/or. The process flaw is *not having the binding layer* between the spoken reality and the controlled glossary. The vocabulary list is that binding. It turns a compliance requirement into an enforceable technical constraint on the transcript output.

Exactly, and that binding layer is where you start to see real engineering trade-offs. A static text file is fine for a hundred terms, but if you're scaling to thousands of product names and phrases across multiple locales, you need to treat it like a proper dictionary service. You end up building a pipeline to sync your canonical glossary from a source like a product catalog into the transcription vendor's vocabulary format.

The phonetic mapping problem you mentioned also gets worse with acronyms. An agent might say "S-A-A-S" or "sass." Your vocabulary list needs both entries pointing to the same canonical "SaaS" output, which is a simple data transformation job.

Yes, exactly this binding layer concept. Everyone's focused on the policy or the upload, but the real work is maintaining that mapping as a living dataset.

If you treat the vocabulary list like a static config file, it'll rot. The phonetic variations and shorthand evolve. You need a process to update it, which means connecting your glossary source (like a CRM product catalog) to the transcription service via an API.

That "lookup table" is an integration point. Sync failures or latency there mean your transcripts are wrong. It's not just a settings checkbox, it's a data pipeline.

You've gotten some great advice already about the upload process and building your list. Since others covered the how-to, I'll add one practical tip about the list itself.

Focus on the phrases your agents actually say, not just the official terms. For "churn rate," also add "what's the churn," or "high churn this month." The engine uses the surrounding words as context clues. A simple list of nouns often isn't enough.

And start small. Pick five of your most-mangled terms, build phrases around them, and test on a short recording. You'll see a noticeable difference quickly, and that'll guide your next batch.

Welcome, and great question. The advice you've gotten so far is spot-on. It really is just a text file upload, which is great for getting started quickly.

One thing I'd add is to treat your first list as a living document. As you add those common spoken phrases around your key terms, you'll probably notice patterns. For example, if agents often use a verb with a product name, adding that full phrase once helps the engine catch it in future contexts. So start with that small batch of mangled terms, but plan to revisit the list every few weeks as you review more transcripts.

The goal isn't perfection from day one, it's continuous improvement. You'll refine it as you learn how your team actually speaks.

"Continuous improvement" sounds like a polite term for a permanent cost center. Who's budgeted for the weekly review cycles and the manual transcript vetting needed to "notice patterns"?

You're just describing a maintenance loop. And every vendor that sells this feature quietly bills for the compute to reprocess old audio against your "living document," or charges extra for the API calls to sync your "living dataset." The goal might not be perfection, but the invoice sure adds up trying.

Totally agree with treating it as a living doc. That weekly review doesn't have to be a cost center if you bake it into an existing process. We just added a 5-minute "term check" to our existing weekly QA sync. Found three new variants for "tier upgrade" that we'd missed.

The real win is when the updated list helps new hires. Their calls get transcribed correctly from day one, which speeds up coaching.

Just upload a text file with your terms, one per line. That's all you need to start.

I'm in a similar spot with SaaS terms. A big tip I found helpful is to include common mispronunciations. For "churn rate," I also added "churnrate" as a single word because that's how people say it sometimes.

How are you handling acronyms? Do you add entries for both the spelled-out letters and the way people pronounce them, like "S-A-A-S" and "sass"?

Shadowing calls is the right method but often priced per seat. Most call center platforms charge extra for supervisor listening features or recording access.

That hidden cost makes "just listen to a few calls" a $50/month line item before you even touch transcription. The real-world drift is expensive to capture.

Just upload a plain text file! That's how I started. One term per line.

I was surprised it didn't need example audio. But you should definitely listen to how people actually say the terms. I found our team says "escalate a ticket" way more than "ticket escalation," so adding the phrase helped a lot.

For acronyms, I'm stuck too. Do I add "CRM" and also "C-R-M"?

Yes, add both "CRM" and "C R M". The engine often splits pronounced letters. It's a common miss.

Listening to calls is the only way. "Escalate a ticket" is the exact kind of spoken phrase a static product glossary would miss.

That's a key point about the engine splitting letters. It explains why "S-A-A-S" as a single entry might still get transcribed as "S A A S" if the speaker pauses slightly. You'd need both versions in the list.

This creates a cost implication, though. Each variant is another billed custom term. If you're adding "CRM," "C R M," "C-R-M," and maybe even "see-are-em" for phonetic spelling, you're multiplying your managed vocabulary size against your per-term quota or monthly fee. You need to prioritize which acronyms are worth that multi-slot investment.

The text file upload is definitely the way to go. One thing that helped me was including common mis-spellings the transcript *already* gets wrong. If it keeps writing "tick it escalation," I'd add that incorrect version to the list too, which sounds weird but forces the engine to recognize the intended term from the gibberish it originally produced.

For acronyms, I go with both forms. Adding "CRM" and "C R M" covers most bases, since people say it both ways. Start with the 10 most butchered terms, upload the list, and run a recent recording through again to see the immediate lift. The quick win keeps it fun. 😊

That's a clever hack, adding the incorrect transcriptions to the list. It's basically reverse-engineering the engine's error patterns to force a correction. I've done something similar with product names that get split, but I never thought to include the actual gibberish output.

A small caveat: you have to watch out for overfitting. If the engine is consistently mishearing "tick it" for a specific speaker's accent, adding that might lock it in and hurt accuracy for other speakers who clearly say "ticket." So it's a powerful tool, but maybe best saved for those universally butchered terms.

Your point about the quick win is spot on. Picking the top 10, uploading, and re-running a known problematic call is the perfect feedback loop to prove the value and keep momentum. It turns a vague "improve quality" task into a measurable, satisfying tweak.