Speaker detection in Fireflies falls apart with more than five or six participants. The transcript becomes a mess of "Speaker 1" and "Speaker 2," making it useless for tracking who said what action item.
I've tried a few workarounds, none perfect:
* **Pre-meeting manual entry:** You can add participant names and emails in the meeting invite details before the call. In theory, this helps. In practice, if people join from different devices or phone numbers, it often fails to map them correctly.
* **Post-meeting editing:** The only reliable method is to manually edit the transcript after the fact, using the "Identify Speaker" function. For a 60-minute meeting with 8 people, this adds a 15-20 minute cleanup task, which defeats the purpose.
* **Separate channels for large meetings:** Some teams record the main session for the overall discussion, then use a dedicated "action items" meeting with only the key 2-3 people speaking to get a clean record. This is a process hack, not a solution.
The core issue seems to be that Fireflies leans heavily on voice fingerprinting, which gets confused with overlapping voices and similar tonal ranges in larger groups.
Has anyone found a functional setup, or is this just a hard limitation we have to accept? I'm comparing notes against other platforms, but the workflow integration here is key.
What's your process for large team meetings?
Your CRM is lying to you.
Hi, I'm also a remote project coordinator at a marketing agency. We have about 25 people and I manage client syncs where speaker tracking is essential. We trialed Fireflies but had to switch because of this exact 6+ person issue.
Here's what I found when looking for a fix, focusing on actual specs:
**Pricing Reality:** Fireflies is about $10/user/month for the Pro plan that does unlimited transcription. Otter.ai's business plan is around $20/user/month but includes 6,000 transcription minutes per user, which covered our needs better.
**Integration Effort:** Both were easy to add to Zoom. The real setup time was in pre-meeting steps. With Otter, we had to make sure the meeting host had the Otter Assistant added as a participant, which was one more click.
**The Breaking Point:** Fireflies' voice ID consistently failed for us after the 6th speaker. With Otter, it started to mix up voices around the 8th or 9th person, but its "Speaker Groups" feature let us manually assign a name to multiple voices at once in post-edit, cutting cleanup time.
**Where It Wins (For Us):** Otter's live transcript during the meeting was more accurate for real-time follow-ups. The shared "Workspace" lets team members correct speaker names collaboratively, which distributed the post-meeting cleanup effort.
If your primary need is a clean, automated transcript for large meetings, I'd look at Otter first. It's not perfect, but the group editing shaved our cleanup time down to about 5 minutes. If budget is the biggest constraint, tell us your per-user limit, and if anyone on your team can reliably handle the post-meeting edits.
Yeah, you've hit on the exact limitation. The voice fingerprinting tech just isn't built for the acoustic soup of a large meeting, especially with any background noise or people on speakerphones.
I've had some success combining your first two points into a forced workflow, but it's clunky. We create the meeting in Fireflies first, manually adding every possible email and phone number for attendees. Then, we *require* everyone to join from the calendar link on their primary device - no dialing in from a conference room phone. It helps, but you still get maybe one or two "Speaker X" entries to clean up instead of a dozen. It's really about reducing the manual edit time from 20 minutes to 2.
Have you looked at the API at all? I've been tinkering with a script that pulls the transcript and compares the "Speaker X" segments against a list of participant join/leave times from the Zoom report to make an educated guess. It's not perfect, but it cuts the manual work down.
api first