Ellis is an AI notetaker for in-person meetings. Record your meeting, get a clean transcript with each speaker identified, then ask anything — what was decided, what you missed, how it went. No laptop. No extra hardware. Just your iPhone (or Apple Watch).
How does it handle background noise and overlapping voices in a real conference room setup?
@ferideayku48864 It uses AssemblyAI to diarize and identify the different speakers. But you should give it a try for your real conference room setup.
In-person notes are a different trust problem than meeting-bot notes. The useful detail is speaker correction and ownership after the recording, because the transcript becomes part memory, part operating record. Getting that boundary right matters.
@krekeltronics 100% agree.
Honestly it's rare by volume but it clusters right where it hurts, the fast back-and-forth when a decision actually gets made, so it feels worse than the raw percentage suggests. I wouldn't chase overlapped-speech separation, it's expensive and still flaky. I'd just surface it: flag the low-confidence diarization spans so I know which two or three lines to re-listen to, instead of trusting a transcript that looks clean. The silent winner-pick is the part that bit me, not the noise.
@dipankar_sarkar thanks!
nice, everyone builds for zoom calls and forgets real meetings exist. curious how it does with a noisy room, that's where my notes always fall apart
@ashmil_hussain give it a try and let me know! What type of noisy rooms are you thinking of? public places?
Curious how it handles crosstalk or people talking over each other in a noisy room. Does the speaker identification still hold up, or does it get messy fast?
@abdulkadirc80x Give it a try. You can start free.
How well does the speaker identification actually work when people are talking over each other or interrupting? That's usually where these tools fall apart for me.
@sedadedemen Interruptions will always be hard in in-person meetings. For virtual it's much easier because they have different audio input.
How well does the speaker identification work in a noisy cafe or group setting with people talking over each other?
@aminekorkf8bw give it a try for free.