During a recent procurement evaluation cycle for a conversation intelligence platform, our team encountered a persistent and operationally significant flaw in Fireflies.ai's automated speaker diarization. In multi-party discovery calls with three or more participants, particularly when voices had similar tonal qualities or when network latency introduced artifacts, the system would consistently mislabel speakers for extended segments, rendering the transcript's attribution useless for downstream accountability and action item assignment.
This presented a critical path issue, as the primary value proposition of such a tool is accurate participant mapping. However, through systematic testing, we discovered a mitigating workflow that is not prominently documented: the speaker labels are **editable in real-time**, during the live call capture, via the active notepad interface. This is a distinct and superior feature compared to the post-call correction process, which introduces lag and recall inaccuracy.
The operational procedure is as follows:
* Access the live Fireflies notepad for the ongoing meeting.
* Observe the real-time transcript stream. Misattributed speech segments will appear with an incorrect participant name or a generic label (e.g., "Speaker 2").
* Click directly on the speaker label for any erroneous segment. A dropdown menu will appear, populated with the meeting's participant list from the calendar invite.
* Select the correct participant. The correction is applied immediately to that segment and appears to influence the AI's model for subsequent similar vocal patterns within the same session.
This mid-call correction capability has substantial implications for procurement and vendor evaluation criteria:
* **Accuracy vs. Workflow Trade-off:** It shifts the evaluation metric from pure, fully automated accuracy to a composite score of "accuracy with minimal human-in-the-loop intervention." The cost of this intervention (a user's time to click and correct) must be factored against the license cost.
* **Training Potential:** There is an open question of whether these manual corrections feed back into the vendor's global model for future calls, or are only session-specific. This distinction is crucial for long-term ROI; a vendor using corrections for continuous learning provides increasing value.
* **Negotiation Leverage:** When discussing contract terms, this feature can be framed as a necessary workaround for a known deficiency. It provides a concrete, documented example to justify requests for service-level credits, extended pilot periods, or price concessions tied to accuracy improvement roadmaps.
For teams considering Fireflies, I recommend explicitly testing this mid-call editing function during the proof-of-concept phase. Structure a test call with intentional speaker confusion scenarios (e.g., multiple team members from the same department) and measure the time-cost of maintaining accuracy through manual intervention. This will generate a realistic total cost of ownership model, encompassing both subscription fees and internal labor.
That's a huge find about editing mid-call. The post-call correction lag is exactly what makes our team abandon transcripts sometimes. We've had similar issues with Fireflies on internal sync calls.
Does this live editing actually "train" the model for that call? Like, if you correct a label for "Sarah" once, does it get better at recognizing her for the rest of that same meeting, or does it keep making the same mistake?
Tested this. The short answer is no, the correction doesn't propagate for the rest of that call. It's a manual patch, not live training.
I fixed a label for "Mark" at the 10-minute mark. The system kept mislabeling him for the next 20 minutes. You have to keep correcting it.
The model seems to lock in its initial diarization profile for the session. Real-time editing is just a workaround to save you from fixing the whole transcript later.
Benchmarks don't lie.
That procedure aligns with our findings, though the real-time editing UI is brittle. If the transcript stream lags by more than a few seconds - which happens reliably during peak network usage - the editable segment often scrolls out of view before you can correct it.
The feature is a tactical stopgap, not a strategic fix. It assumes you have the bandwidth to monitor and correct a live transcript while actively participating in the meeting, which isn't viable for most facilitators.
You're still left with a fundamentally broken diarization engine.
Your fancy demo doesn't scale.
Great question about the live training. I can confirm what user518 found: those mid-call edits don't retrain the model in real-time. It's purely a manual override.
That said, I've noticed the corrections *do* seem to persist if you have a recurring meeting with the same participants. I've edited labels for a teammate in our weekly standup, and by the next week's call, the initial diarization was slightly better. It feels like the training happens offline, after the call is processed. But during the live session? You're just patching holes in a sinking ship.
Makes you wonder if they're prioritizing batch model updates over live inference because it's computationally cheaper.
pipeline all the things
Thanks for putting this to the test. Your point about it being a workaround to save time *later* is exactly right - it just moves the tedious correction work into the meeting itself.
I've seen teams get tripped up thinking a mid-call edit is a permanent fix for that session, only to have the transcript still be a mess when they go back to review it. It really does lock in that initial profile, as you said.
Keep it constructive.
The real-time editing you found is critical, but it's dependent on a flawless network connection to their transcription service. In our trials, any jitter or packet loss - which is common on corporate VPNs or shared conference room wifi - causes the editable transcript window to desync from the actual audio by 10-15 seconds.
At that point, you're trying to fix a label for a conversation segment that has already scrolled out of the UI. The feature becomes unusable, and you're forced into the post-call cleanup anyway.
It solves the problem only under ideal network conditions, which isn't realistic for remote teams.
Automate everything. Twice.
That's a fascinating find about the real-time edit capability. It makes me wonder how much of a "live" model we're actually dealing with.
If the initial diarization is locked in for the session, then the transcript we're editing is just a live *display* of a pre-processed output, not a live *inference*. The ability to patch it suggests their UI is simply applying a manual filter on top of a static analysis. It's more of a live annotation layer than a true correction of the core diarization engine.
This leads to a bigger question: if the real-time edit is just an annotation, does that corrected label actually persist into the final, processed transcript data, or is it a view-only fix that gets lost when the call ends and the system re-processes the audio with its original, flawed model? I've seen other tools where the "live" view and the final artifact are generated by completely different pipelines.
The real-time edit you found is useful, but it introduces another failure mode: the UI's auto-scroll behavior.
If you correct a label for a segment that's currently visible, but the transcript is updating quickly, the act of clicking 'save' can cause the entire viewport to jump to the latest line. You lose your place. Now you're fighting both the diarization and the interface just to apply a manual fix.
It turns a 5-second correction into a 30-second hunt-and-peck, which defeats the purpose of doing it live.
Build once, deploy everywhere
You've hit on a key architectural question. The distinction between a live annotation layer and a true correction of the inference engine is critical.
From my own testing, the corrected labels do persist into the final transcript artifact. However, this persistence is purely a data override in their storage layer, not a re-run of the model. It's as if they're storing your manual edits as a separate metadata patch file that gets applied on top of the original, flawed diarization output.
This creates a brittle dependency. If their post-call processing pipeline ever re-generates the base transcript from the raw audio - say, during a version upgrade of their speech-to-text model - those manual patches could become orphaned or misaligned. I've seen this happen once after a major platform update, where all previously corrected transcripts reverted to their original, incorrect state because the underlying segment timestamps shifted.
You've documented a valuable workaround for a common issue, but calling it "superior" to post-call edits might overstate the case. It swaps one set of problems for another, as a few other users have pointed out.
This real-time process assumes a perfect meeting environment: no network lag, and enough spare attention from the facilitator to monitor and correct the transcript live. In practice, that's a tall order.
The real value of your discovery is that it confirms the diarization is effectively 'frozen' at the start of the call. That's useful to know for anyone evaluating their workflow. It means the feature isn't self-correcting; it's just a manual annotation tool they've made accessible during the session.
Keep it constructive.
Exactly. Calling it a "superior" workflow misses the reality of cognitive load during a meeting. The facilitator's primary job is to guide the conversation, not to become a live transcript editor.
> It means the feature isn't self-correcting; it's just a manual annotation tool they've made accessible during the session.
This is the key architectural takeaway. It's a UI convenience feature, not an improvement to the core diarization model. The persistent "annotation layer" model user918 mentioned explains why it feels like you're just putting sticky notes on a flawed document.
For teams, the question becomes whether investing that in-meeting attention is a worthwhile trade-off versus a dedicated 10-minute cleanup afterward. For most, I suspect it isn't.
"Superior" is a stretch. You've traded post-call drudgery for the cognitive load of live transcript janitor duty during the meeting itself.
The core problem is that this isn't a fix for the diarization model, it's just a UI band-aid. You're manually curating a broken output in real-time, which distracts from the actual conversation. In a procurement evaluation, that's a hidden cost you need to factor in - how much facilitator attention is being siphoned off to correct their tool's failure?
If the primary value proposition is accurate participant mapping and it consistently fails in multi-party calls, you're not mitigating a flaw. You're documenting the workaround required to make a fundamentally unreliable product barely functional. That should be a major red flag in your evaluation, not a feature highlight.
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Oh, that "permanent fix" assumption is a classic trap. I've been there. You finish a call, you spent half of it clicking labels, you think "Great, that's done," and then the exported document shows Speaker 1, Speaker 2, and Speaker 1 again. The worst is when you're the one who promised the clean transcript to the team. It's a real face-palm moment that tends to only happen once before you learn that lesson the hard way. It definitely cements that the initial guess is what the system is married to for the whole session.
it worked on my machine
Your emphasis on this being a "superior feature compared to the post-call correction process" is the part I find operationally questionable. The premise assumes the real-time editor is a reliable mitigation, but that ignores the critical latency factor between the audio stream and the transcription service's processing loop.
In our load tests, we measured this delay at a median of 8.2 seconds under optimal conditions. This means you're not editing a live transcript; you're editing a stale representation. By the time you see and correct a diarization error, the conversational context has already moved on, making accurate correction cognitively expensive. You're essentially performing a manual merge of two asynchronous streams, which introduces its own class of errors.
The workflow you describe may be viable for a small, static call, but it doesn't scale to the dynamic, multi-party discovery calls you mentioned, where facilitator attention is already at a premium.
--perf