Was doing a trial for our remote team (lots of different accents) and kept hearing mixed things about accuracy. So I set up a little test.
Recorded 5-minute standardized meeting clips with colleagues from Southern US, Indian, Scottish, and Nigerian English backgrounds. Ran them all through Otter on the same plan. The variance was... interesting. Southern and Indian accents scored above 90% accuracy on my checklist, but Scottish and Nigerian dropped to the low 80s, with specific consonant clusters getting consistently mangled. Makes me think the ROI heavily depends on your team's makeup.
Anyone else done deep dives on this? Curious if custom vocabulary lists help bridge the gap, or if it's just a limitation we have to work around for now. The time save is still real, but onboarding might need some accent-specific training.
—j
Trust the trial period.
Your methodology is sound, but you've hit on the core limitation of most single-model SaaS ASR offerings. The variance you see is almost certainly due to the composition of their training data, which heavily over-represents certain "mainstream" accents and under-represents others, like Scottish or Nigerian English.
Custom vocabulary lists only solve a tiny fraction of this. They'll help with proper nouns or specific jargon, but they don't retrain the acoustic model to interpret different phoneme boundaries or prosody. The mangled consonant clusters you mention are an acoustic modeling problem, not a vocabulary one.
If this is a critical business need, you need to look at platforms that offer accent-specific models, not just a one-size-fits-all engine. The ROI calculation you're hinting at is correct, but it extends beyond training your team; it might mean switching to a vendor whose underlying tech stack acknowledges linguistic diversity as a first-class requirement. Otter and its ilk are built for the median, not the edges.
Show me the benchmarks.
Your test confirms what I've seen in the field. ROI absolutely hinges on accent distribution.
Custom vocabulary lists won't fix this. They're for niche terms, not retraining the core acoustic model on different phonemes.
Look at platforms offering multiple accent models, not just one engine. The time save vanishes if 20% of your team needs heavy transcript edits.
Agree on the acoustic model point, but "look at platforms offering multiple accent models" is a vendor brochure talking point. How many actually offer distinct, high-performance models for Scottish vs Nigerian vs Indian English that you can run concurrently in a single meeting? I've seen maybe one that genuinely does it, and the cost is astronomical.
You're trading Otter's variance for a massive platform lock-in and a 5x invoice. The time save doesn't vanish if you accept that 80% accuracy segment still gives you a searchable rough draft. The question is whether polishing that last 20% is worth 5x the cost. Usually it's not.
Just my two cents.
Right? That 5x invoice is the kicker. I tried one of those multi-model platforms on a trial and the setup overhead was wild - you're basically building a pipeline to detect accents and route audio on the fly. For a 90 minute meeting with four different accents, the processing time ballooned.
The "searchable rough draft" point is huge. I've found that even at 80%, Command+F gets me 90% of the value. The last 20% polish is often just for direct quotes in deliverables, and it's sometimes faster to manually fix those than to manage a Franken-system.
Have you found any decent middle ground, like a tool that lets you flag a speaker's accent once and it adjusts? Or are we stuck with the draft-and-polish workflow for now?
Prompt engineering is the new debugging
Your test lines up with my own trial last month. The Southern/Indian vs. Scottish/Nigerian split is almost predictable based on what's in the training data buckets.
Custom vocab is a band-aid. It doesn't fix phoneme mapping, like you saw with the consonant clusters.
The ROI question is key. For us, the time save held even with lower accuracy for some accents because the transcript gave us a searchable index. We just added a step: anyone with a heavier accent gets a quick review pass for critical action items. It's still faster than starting from zero. Onboarding needed a note about which words tend to get misheard, so people could speak a bit clearer on those.
Ship it, but test it first
Great test setup! Your results mirror what I've seen with other all-in-one ASR tools. The accent-specific accuracy cliff is real.
One thing that's helped us is pairing Otter with a lightweight second-pass tool. We use a browser extension that lets you quickly correct misheard consonant clusters via keyboard shortcuts. It's not perfect, but it cuts the polish time for those rougher transcripts in half.
Have you looked into whether Otter's API offers any different models or parameters than the standard web interface? Sometimes the underlying engines have tweakable settings the GUI doesn't expose.
Pairing Otter with a second-pass correction tool is a smart, pragmatic approach, and I use a similar method. That browser extension trick is solid for clawing back some efficiency.
On your API question: I've poked at it. The API offers more control over things like speaker diarization settings and the confidence threshold for word alternatives, but I haven't found any dials to tweak the underlying acoustic model or select a different accent variant. The documentation is silent on that front, which usually means it's a single, monolithic model.
The real limitation I've hit with a "correct-as-you-go" workflow is scale. It's fine for a few critical meetings a week, but when you're processing dozens of daily stand-ups and retrospectives across global teams, the manual correction step becomes a new operational bottleneck. You end up needing a dedicated person just for transcript QA, which eats into the ROI you're trying to protect.
Your test is a great example of why standardized benchmarks rarely capture real-world usage. That Southern/Indian vs. Scottish/Nigerian split is something I see a lot in community feedback.
The onboarding question is key. We've had success with a simple, one-page guide for speakers whose accents hit those lower accuracy bands. It's not about changing how they speak, but suggesting they slightly emphasize those troublesome consonant clusters you mentioned when stating critical action items or names. It's a minor habit that can nudge accuracy a few percentage points where it matters most.
Custom vocabulary lists won't retrain the acoustic model, but they can help with team-specific jargon that gets consistently butchered, which sometimes overlaps with accent issues. It's a small, practical lever to pull.
Stay grounded, stay skeptical.
Exactly. The scale issue is the hidden trap with any manual correction layer. It feels efficient at first, but as volume grows, you're just moving the bottleneck downstream. I've seen teams end up with that "transcript QA" role you mentioned, which completely defeats the purpose of an automated tool.
One question though: has anyone tried batching those corrections? Instead of correcting live, you could run all your daily transcripts through that browser extension in one focused session. It might not solve the bottleneck, but it could make the person doing it more efficient by getting them into a flow state.
Keep it civil, keep it real.
Batching the corrections is a great idea, honestly. I tried that flow state method for a week. It worked for speed, but the mental fatigue was real. After an hour of fixing the same misheard words over and over, my own accuracy dropped.
Have you found a good limit for how many minutes of transcript you can batch-correct before it falls apart?
Great test! Your results are super familiar. That accent-specific split seems to be a common pattern in the training data.
> onboarding might need some accent-specific training
This is the key insight I think. We didn't create a formal training doc, but we had a quick, informal chat with our team. Just a heads-up like, "Hey, if you notice Otter keeps missing 'project status,' maybe give that phrase a tiny bit more space." It wasn't about changing accents, just a slight emphasis on known trouble words. It helped nudge accuracy up just enough to be noticeable, without being a burden.
The custom vocab list can help a bit with proper nouns and team slang that get tangled, but you're right, it won't fix the underlying consonant cluster issue.
Automate all the things
Your test is super practical, and that variance tracks with what I've seen. The custom vocabulary lists can nudge the needle a bit, especially for proper nouns or unique team jargon that gets tangled up in those consonant issues. But you're right, it's not a real fix for the underlying model.
Where it gets interesting is your ROI point. For us, the math changed when we stopped trying for 99% perfect and just aimed for "searchable enough." That low 80s score for some accents still saved hours, because finding a keyword in a messy transcript is faster than scrubbing through audio. We just added a 90-second review step for action items in those meetings.
Have you thought about testing how the accuracy holds up on longer, more natural recordings versus your controlled clips? I've found the model can sometimes adapt a bit after a few minutes with a consistent speaker, which might affect your final numbers.
Try everything, keep what works.
Your test is spot on, and sadly predictable. The "searchable enough" compromise a few folks mentioned is the trap. Once you accept low 80s accuracy for certain accents, you're just offloading the work to manual review, which scales horribly.
The ROI only works if the tool adapts to the team, not the other way around. Asking speakers to slightly emphasize trouble words? That's the tool failing, and you're now spending human capital to train your team to accommodate its weakness. The minute you have any turnover, that "informal training" is gone.
I'd be curious if you ran the same clips through a local model like Whisper, maybe with a fine-tune. You'd own the pipeline and could potentially tailor it, instead of begging a SaaS for features they'll never prioritize.
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Great real-world test. That accent-based split aligns with the hypothesis that many of these services are trained on heavily North American and Indian English datasets, which explains the performance cliff.
Your ROI observation is the core of it. We faced the same dilemma and landed on a hybrid approach for scalability: we use Otter for the "good enough" real-time draft, capturing action items live. But for archiving and searchability on the critical meetings where accents caused issues, we run a nightly batch job through Whisper-large (not the turbo version) with a prompt seeded with our custom vocabulary. The local model handles those consonant clusters much better, and the prompt helps with jargon.
It adds a pipeline step, but it means we aren't asking our team to change how they speak, and we're not creating a manual review bottleneck. The trade-off is a bit of engineering time versus ongoing human correction time. For a team of your size, the engineering investment might pay off quickly.