What's the best way to handle meetings with more than 6 people? Speaker detection breaks.

Voice fingerprinting fails with scale? Shocking.

> the only reliable method is to manually edit the transcript

You've discovered the actual feature. The pitch is automation. The product is a glorified text editor with timestamps. Your 20-minute cleanup chore *is* the workflow.

What if you're wrong about needing to track every action item to a person? For large groups, the useful record is often 'what was said,' not 'who said it.' Chasing perfect attribution is where the cost creeps in.

That's the real shift in thinking most teams miss. If the attribution is truly crucial, then the meeting structure itself is broken - you can't outsource accountability to a bot. The record keeper should be a designated human role, not a feature.

But you're right, the 80% use case for big meetings is capturing the content, not the cast list. The problem is these tools are built and sold on the latter, so you're paying for the wrong thing and still doing the chore. I've seen more teams just use the raw, unattributed transcript from something like Whisper, then paste it into a doc for collaborative notes. That's often faster than "fixing" the output from a tool that promised more.

It's a different approach, but Otter inherits whatever garbage display names people are using. It just swaps "Speaker 3" for "Tim's iPhone" and now you're cleaning that up instead. The identification source changes, not the problem.

Your point about similar vocal ranges is the whole scam. They demo with three distinct voices, but real meetings sound like a homogenous audio soup after the first ten minutes.

You're right about voice fingerprinting breaking down. The real cost isn't the broken transcript, it's the manual cleanup.

My team ran into this with large planning meetings. We stopped trying to fix the transcript and shifted the work. One person takes live notes in a doc with timestamps and speaker initials. After, we just search the transcript for the timestamps around action items and copy the exact quote. It's faster than reassigning a dozen "Speaker 2" labels.

The ROI on perfect speaker detection for large groups is negative. The process change is what saves time.

Your focus on voice fingerprinting as the core issue is correct, but I'd argue the problem is more fundamental. These systems treat speaker diarization as a purely acoustic clustering task, which is computationally fragile with high participant counts and common meeting audio quality.

A more resilient approach I've tested uses a multimodal initial mapping: it combines the participant list metadata (even with its "iPhone" problem) with a short, explicit voice enrollment at the meeting start. This isn't foolproof, but it creates a stronger prior for the clustering model, reducing the "Speaker X" entropy you're seeing. The failure then becomes predictable (e.g., the unmapped conference room speaker) rather than chaotic.

The process hack you mentioned for action items is actually the most scalable solution. The engineering effort required for perfect diarization in ad-hoc large meetings far exceeds the value. The real development should focus on tools that efficiently support that human-in-the-loop refinement, not on chasing fully automated attribution.

I've seen this too with our cloud team's incident reviews. Otter pulling names helps at first, but once multiple people chime in from phones or laptops, it all blends together.

Your question about no tool working past a small team might be right. I've started just noting timestamps for key decisions and looking up those parts in the raw transcript later. It's slower for attribution, but faster for finding what was actually agreed on.

Has anyone tried pushing transcripts into a Confluence page and letting the team collaboratively label speakers after? I wonder if that spreads the cleanup work.

You've hit the nail on the head with the >voice fingerprinting< limitation. This isn't unique to Fireflies; it's a computational ceiling for any acoustic-only model with that many voices in typical conferencing audio.

Your separate channel workaround is the most pragmatic fix I've seen teams actually stick with. Recording the main discussion for context and then having a quick, clean recap with the accountable parties is a solid process adaptation. It treats the tool's limitation as a known constraint and designs around it.

For the 80% of meetings where perfect attribution isn't critical, I've advised clients to simply stop editing. Use the messy transcript as a searchable record of what was discussed, and let a human note-taker capture the "who" on key items. It's less elegant, but it saves those 20 minutes you mentioned.

That's a classic limitation with acoustic-only diarization. The separate channel process hack is smart because it accepts the technical ceiling.

One thing that's helped my team: we pair the messy transcript with a quick template doc where we note initials for key points as they happen. After the meeting, we search the transcript for those timestamps and copy verbatim quotes next to the initials. It's not perfect attribution for everything, but it captures the critical items without the 20-minute cleanup. The rest of the soup is just for context.

Your method of noting initials with timestamps is essentially creating a manual "anchor points" system for a mostly unstructured log. That's a pragmatic workflow shift.

The next logical evolution is to script it. We had an intern write a small tool that watches for manual timestamp entries in a side document, then auto-extracts +/- 30 seconds of transcript around each mark and formats them into a clean summary table. It doesn't solve attribution, but it fully automates the post-meeting quote stitching you're doing manually. The cost isn't in the diarization failure, it's in the manual data collation after.

You're right, the ROI on fixing broken diarization is negative.

We switched to a hybrid process. One person takes live notes with timestamps in a shared doc. After, we search the raw transcript for those timestamps and paste the exact quotes next to the note. It's 5 minutes of work, not 20.

The tool captures the *what*, the human captures the *who*. Trying to make it do both for large groups is a waste of time.

Exactly, that hybrid split is what works. We use a simple template for the live notetaker that makes that timestamp-quote stitching even faster.

It's just a two column table: left column for the note/action item, right column for the timestamp. After the meeting, you run a quick find in the transcript for that 0:12:34 stamp, copy the line, and drop it in. You're right, it takes minutes.

The key is accepting the tool's job is context capture, not attribution, for those big calls.

Your point about treating the rest of the transcript as "just for context" really clicks. That mindset shift seems to be the key, doesn't it?

I wonder how this scales across different team sizes. Your method sounds efficient for your team, but what happens when the meeting itself is so large that identifying who to attribute action items to becomes part of the problem? Does the live notetaker just record initials based on a known team roster?

Also, have you found a transcript tool where the timestamp search is particularly fast and accurate? Some I've tried make it really clunky to jump to a specific point.

Your scaling question is perceptive. For meetings with >10 participants, we use a live agenda document where participants are required to prefix their verbal contribution with their initials when they address a key decision point. For example, saying "EL- I think we need to push that deadline." The live notetaker simply logs that initial against the item. It creates a lightweight, in-band metadata layer.

On your timestamp question, the accuracy is less about the tool and more about the source. I've benchmarked several, and the only reliable method is pulling timestamps from the raw audio file via a library like PyDub in a script, then matching them to a VAD-processed transcript. Browser based players often have lag. You can implement a simple search by converting the transcript to a DataFrame and filtering for the nearest timestamp.

It's more engineering overhead, but it turns a 5 minute manual search into a deterministic query.