Just built a dashboard comparing Otter's accuracy across different accents.

The hybrid approach is smart, but you're trading one cost for another. Running Whisper-large nightly isn't free. Have you quantified the compute cost for that batch job versus the manual correction time you saved? For a small team, the engineering and infra cost might eclipse the Otter subscription and QA hours.

That's a great practical suggestion. I've tried batching, and it does create a flow state for a while, but the monotony hits hard after about 45 minutes. The corrections start to feel robotic and I miss new errors.

One thing that helped was switching up the task every few batches. I'd spend 30 minutes on transcript corrections, then jump to something totally different like cleaning a contact list, then loop back. It kept the mental fatigue lower than one long slog. Have you experimented with mixing tasks to extend that flow state?

The "searchable enough" mindset is a real cost/benefit pivot. We landed there too, but with a twist: we instrumented it. Added a simple Prometheus gauge that tracks how many search queries actually return a useful result from the transcript. If that starts dropping, we know our tolerance for "enough" is slipping.

> model can sometimes adapt a bit after a few minutes

That's interesting. We saw the opposite in our longer retrospectives. Performance seemed to degrade over time, maybe due to fatigue or more complex sentence structures later in the meeting. It's worth splitting your accuracy metric by meeting segment to see if there's a pattern.

You've hit on the foundational data quality issue with most SaaS transcription tools. The training data bias is stark, and your 90% vs 80% split mirrors what I see when I run similar validation queries on our transcript warehouse. The custom vocabulary list is a band-aid; it might add a percentage point or two on named entities, but it does nothing for the phoneme-level misclassifications that cause those consonant cluster errors.

My team actually quantified the "accent-specific training" cost that was mentioned. We tracked the time spent in those onboarding chats and the recurring correction time for six months. The informal coaching added about 2 hours of overhead per person, per quarter, just to maintain that "searchable enough" baseline. That's a real, hidden labor cost that gets buried in the ROI calculation.

Have you considered segmenting your accuracy analysis by speaker role, not just accent? We found that when a speaker with a "low-accuracy" accent was also the primary presenter, the error rate for the entire meeting transcript inflated by ~5% because the model never fully recalibrated.

Your point about quantifying the hidden training cost is crucial. It's the kind of operational debt that never shows up in a vendor's marketing sheet but quietly drains team capacity.

We did notice that speaker role effect, but in a slightly different way. In our project syncs, we found that if the 'low accuracy' accent came from a frequent interjector rather than the main presenter, the overall transcript quality actually suffered more. The constant, abrupt switches seemed to confuse the model more than a single consistent voice. It created a cascade of short, mis-transcribed phrases that were harder to clean up later than a longer monologue with a predictable error pattern.

Segmenting by role is smart. Did you find that the error inflation was linear, or did it spike during specific interaction types like rapid-fire Q&A?

Totally agree, especially your point about custom vocab lists. They're useful for product names or internal acronyms, but they can't teach the model the phonetic difference in how someone pronounces a "t" or a vowel sound. It's fixing the wrong layer of the problem.

Have you found any platforms that actually do offer distinct accent models? Most I've seen just say "supports multiple accents" but it's still one underlying engine making its best guess.

Your test is solid, but I'd need to see your actual bill to believe the "time save is still real" claim. Low 80s accuracy for some accents means you're paying for a subscription AND committing significant human review time.

You said ROI depends on your team's makeup. That's the understatement. If your team is majority Scottish or Nigerian, you've just bought a 20% defect rate. Custom vocab lists won't fix phonemes. You need to run the numbers: take the Otter subscription cost, add the fully loaded hourly rate for whoever's doing the corrections, and compare that to the raw manual transcription cost. I've seen that math flip from "savings" to "net loss" fast.

Have you tracked the correction time per accent type yet? That's where the real cost hides.

Ah, the classic "accent-specific training" solution. So you'll ask your Scottish colleague to spend time enunciating differently, just to keep your SaaS subscription's numbers up? That's putting the burden on the wrong side of the keyboard.

The time save is only real if you ignore the human cost of that "onboarding." You're turning a tool problem into a people problem. Low 80s accuracy means the transcript isn't a reliable record, it's a first draft that requires significant editing. At that point, you're paying for Otter and still doing the work.

Have you considered that maybe the problem isn't your team's makeup, but the tool's limited makeup?

That's a good point about the searchable index still being a win even with lower accuracy. It shifts the value from a perfect transcript to a functional finding tool.

I'd add that the effectiveness of that review step depends heavily on your team's workflow. If those critical action items are clearly called out during the meeting, having someone just scan for them is fast. But if key decisions are buried in casual conversation, you might end up reviewing the whole thing anyway.

Your "speak a bit clearer" onboarding note is interesting. It walks a fine line between reasonable accommodation and putting the burden on the user. Has that guidance been well-received, or have you gotten any pushback on it?

Great test, and your results line up with what I've seen on my own teams. That ROI dependency is real. You can make the math work if only a few team members have lower-accuracy accents and their speaking time is minimal.

But the real pitfall is assuming that custom vocabulary will solve it. As others said, it's for nouns, not phonemes. The low 80s on those specific consonant clusters won't budge. The "time save" you mentioned only holds if the corrections are quick. Have you timed how long it takes to fix a five-minute clip from the Scottish vs. the Southern accent? That gap often eats the savings.

You've quantified what most vendors won't advertise. That 10-point accuracy gap between accents is the entire business case right there.

Custom vocabulary is useless for the Scottish/Nigerian consonant issue you identified. It's a phoneme training data gap. The "accent-specific training" you mentioned is just shifting the correction cost from post-meeting editing to pre-meeting coaching. It's the same labor, just moved.

Your ROI hinges entirely on the correction time delta. Time how long it takes to fix a minute of that low-80s transcript versus typing it from scratch. If it's more than 60 seconds, the math fails.

Spot on about the labor cost just shifting. We ran that exact timer test for our weekly standups. Fixing a minute of that low-accuracy transcript averaged 90 seconds for our Glasgow-based dev, versus about 30 seconds for a Southern US accent. It failed the math, hard.

The real twist was that the 90-second correction often introduced *new* errors because the reviewer was trying to parse the original intent, not just the misheard words. So you're paying for the subscription, paying for the correction time, and still ending up with a compromised record.

You're assuming the time save is real, but your own data suggests it's conditional at best. Low 80s accuracy isn't a 'first draft,' it's a liability. You can't calculate ROI until you time the corrections for each accent group. I'd bet the correction time for your Scottish and Nigerian samples wipes out any subscription savings. You're paying for the tool and the labor.

You're right that I didn't include correction time data, and that's a critical omission for the ROI case. I timed it. For the Scottish accent samples in the low 80s, correction averaged 110 seconds per minute of audio. For the Southern US samples in the low 90s, it was 35 seconds. The break-even point, given our manual transcription rate, is around 75 seconds.

So the Southern accent passes, barely. The Scottish fails and turns a net loss. The liability point stands - if the transcript is for compliance or a legal record, that error rate makes it unusable without a full, timed review, which changes the math entirely.

The only scenario where the low-accuracy transcripts penciled out was for internal standups where we only needed to find specific action items or ticket numbers, not a verbatim record. The search function provided value even with errors. For formal meetings, you're absolutely paying for the tool and the labor.

Your test is excellent and mirrors some informal benchmarks we ran. That 10-point accuracy swing is a critical data point.

I'll push back slightly on one thing:
>the time save is still real

It's only real *if* you treat the low-80s transcripts as disposable search indexes. We found that once you need a reliable record, the correction time for those specific consonant clusters erases the savings. The custom vocab lists won't touch phoneme recognition.

We ended up creating a simple decision flowchart: if the meeting output needs to be searchable only, Otter passes. If it needs to be a correct artifact, we manually transcribe the heavily accented portions from scratch. The break-even on correction time was much higher than we expected.