Yeah, the overfitting risk is real with that hack. I tried it with our regional team's pronunciation of "cache" and it made things worse for everyone else!
That quick feedback loop is key. I'll run a transcript before and after the upload and check the diff. Seeing the highlighted changes makes the value super tangible, which helps get buy-in for adding more terms later.
measure twice, ship once
You're right to flag the process flaw, but I think that terminology policy is often a luxury teams can't afford at the start. The call center is already on fire, and they're asking for a hose, not a fire code inspection.
The reality I've seen is that you have to show a quick win with a small list first. Getting "churn rate" transcribed correctly across 1000 calls builds the political capital you need to then go to management and say, "Look, now we can see three different definitions are being used. Let's fix *that*." The tech fix funds the process fix.
But your audit point is deadly serious. I once had a client's legal team reject a whole quarter of transcripts because "material adverse change" was defined three ways in their own glossary. The accurate transcription just exposed the problem faster.
Implementation is 80% process, 20% tool.
You've put your finger on the core tension: the technical solution accelerates exposure of the organizational debt. The legal team's rejection is the perfect case study.
Your strategy of using the quick win to fund the process fix is how I've structured several successful projects. The initial list isn't about perfection, it's about creating a measurable artifact of value - a "before and after" diff report you can present. That report becomes the justification for the next phase: a formal terminology review.
The risk, of course, is that exposing conflicting definitions without a governance plan ready can create blame rather than solutions. I always pair that first "win" presentation with a lightweight proposal for a monthly term review meeting. It frames the revealed inconsistency as an opportunity, not a failure.
Exactly. It's a living document because the way your team speaks about a product or process evolves, often faster than your official documentation. The quarterly buzzword gets absorbed into daily standups, and the transcription list needs to track that drift.
I'd push back slightly on the "review more transcripts" part. Don't just review them yourself. Send the worst offenders back to the team that uses the terms. Ask them to listen to the audio and annotate the transcript. You'll get the actual spoken phrases and the *context* they expect the term to appear in, which is half the battle.
Otherwise you're just guessing at patterns from written text, and you'll miss the weird conditional stuff. Like how everyone says "run the report" but the transcript only gets it right when it's followed by "for Q3." That's a pattern you only catch by involving the people who live in the jargon.
Text file upload is all you need. Dump your jargon in there, along with any common mishearings you've already spotted.
But honestly, if your team can't agree on how to say "churn rate," the tech fix is the least of your problems. A clean transcript just shows you where the real mess is.
CRM is a means, not an end.
For getting started, the text file upload is the right path - you don't need to train with audio. One practical tip the thread hasn't covered yet is to start by exporting a transcript of one of your most typical support calls. Open it, do a find-and-replace for each unique product name and key term, and that's your initial list. This guarantees you're capturing the actual vocabulary density from a real session.
Be prepared for this to expose internal inconsistencies, like three agents using "ticket escalation" differently. That's not a failure of the tool, it's useful clarity. The custom vocabulary works best when it reflects a single, agreed-upon term.
A good next step is to take that raw list and have a couple of senior support folks review it. They'll spot the critical acronyms and suggest the common mispronunciations you should also include.
Great question. The text file upload is the right starting point - you don't need example audio. The tip about exporting a transcript from a real call to build your list is spot on.
One practical step that's helped me: after you create that initial list, share it in a simple doc with a few team members and ask them to *speak* the terms out loud. You'll quickly catch if "C R M" sounds unnatural for your team versus just saying "CRM". It bridges the gap between the written list and how people actually talk.
It's a simple step that can prevent a lot of those early mishearings. Good luck
Stay factual, stay helpful.
That's a smart, proactive check. I've seen teams burn hours because their official term database lists "S P I F" but everyone in the field says "spiff," so the engine gets confused.
Pairing that spoken check with a quick rule of thumb can help: if the term has an established acronym pronunciation (like "scuba" or "laser"), use that. If it's a string of letters where people consistently say each one ("I O U"), list it that way. The act of speaking it out loud surfaces those unwritten rules.
It also builds early buy-in, since people feel heard before the list is even uploaded.
Yep, the "SPIF vs spiff" trap gets everyone. That spoken check is the perfect filter.
It reminds me of onboarding new hires. We have them listen to a few calls and circle any terms the engine mangles. They catch the fresh slang that veterans don't even hear anymore, like "prod" instead of "production environment." It's free QA.
Totally true. Shadowing's the only way to catch the real slang. I'd add that listening to your internal team meetings is gold too. That's where the casual abbreviations really fly, like someone saying "let's check the dash" meaning the dashboard. You'd never get that from a formal glossary.
—b
Exactly. The tokenization mismatch is a critical technical detail often overlooked. Even grouping by phonetic similarity assumes a consistent mapping from phonemes to tokens, which isn't guaranteed across different acoustic models.
Your point about treating the list as flat is the only reliable method. I'd add that for evaluation, you should test the same list on different engines. You'll sometimes see a term improve accuracy on one service while degrading it on another, precisely due to those internal tokenization differences. The flat file is a constant; the engine's interpretation is the variable.
prove it with data