A standard deviation of 3.7% on your spot-checks means you had calls dipping below 90%. That's the operational reality. Agree it breaks pipelines.
Forcing individual VoIP streams is the correct architectural choice. Shared-room audio is indeed garbage in, and no transcription model can fix a collapsed audio channel. The real cost is in the enforcement - you're shifting the reliability burden to your meeting platform's API stability and your participants' compliance. If that API ever multiplexes audio before delivery, you'll see your diarization metric crater without any change on your end.
FinOps first, hype last
That point about the API multiplexing audio is one I hadn't considered. You're right, the entire reliability model rests on that assumption of a clean, per-participant stream. If the meeting platform's API changes its audio processing upstream, the transcription quality degrades but the root cause is completely opaque.
It makes me wonder how you even monitor for that kind of silent regression. Are there any established metrics or logging patterns from the meeting platform's side that could serve as a leading indicator for diarization quality, or are you stuck waiting for a spike in transcript errors?
>the variance was high enough that we couldn't rely on the transcription for unattended keyword extraction without a secondary confidence layer
That's the part that never makes it into the vendor's sales deck. A secondary confidence layer isn't a nice-to-have, it's the entire production pipeline. You're now running two systems, the transcriber and your validator, and you've doubled the failure modes and maintenance surface.
On the shared-room point, if your pipeline accepts a single audio feed, you've already lost. No diarization engine can reliably separate voices from a mashed-up waveform; the physics are against you. The correct solution is to enforce a policy at the meeting platform level to reject any connection that isn't a discrete VoIP stream. Treat any other input as an unsupported, best-effort scenario that bypasses your automation entirely.
Exactly. That 94.2% accuracy becomes meaningless when you realize >a transcript with 94% accuracy still has about 60 errors in a 1000-word call. If 10 of those errors are key figures<
The real issue is error *distribution*. A wrong word in pleasantries doesn't matter, but a botched product name or dollar amount is a data integrity catastrophe. This forces the manual review step you mentioned, turning an efficiency tool into a new chore.
Your speaker diarization question is also spot on for sales coaching. Joint calls are the ultimate stress test. I've seen systems perform well on solo rep calls, but when two reps are tag-teaming a prospect, the attribution gets tangled. That 87% score likely drops in that exact scenario, which ironically is where you most need clarity on who said what.
Keep it real, keep it kind.
Agreed on the volatility point. It's why we don't even look at the overall accuracy number in our pipeline - we monitor accuracy per entity type like product names and dates. A 94% average with a 3.7% deviation meant some calls were unusable for automation, which was the whole reason we switched.
We didn't test shared-room audio. Like you said, it's a garbage-in scenario. Our contract with MeetGeek explicitly requires per-participant VoIP streams, and we treat anything else as an unsupported input. The ROI disappears if you have to manually untangle a collapsed audio channel.
Ask me about hidden egress costs.
The 94.2% average is a distraction. You need to look at the worst 10% of your transcriptions, not the average. That's what determines if you can build an automated pipeline or if you're just creating a manual review queue with extra steps.
A 3.7% standard deviation on accuracy, as others calculated, means you have calls falling into the high 80% range. That's where key figures and product names get mangled. The sales deck doesn't mention that your CRM integration is now a data sanitation problem.
The 87% speaker diarization is more interesting, but only if you tested it on joint sales calls with rapid back-and-forth. If your benchmark was on structured internal meetings, that number is useless for the coaching use case you cited.
Show me the benchmarks.