Having utilized MeetGeek for several months to record and analyze technical meetings for my benchmarking workflows, I have been conducting a systematic evaluation of its transcription accuracy, with a particular focus on speaker diarization. After reviewing approximately 15 hours of recorded meetings involving 3-5 participants each, I must conclude that the speaker labeling functionality remains fundamentally unreliable for any serious, data-driven use case.
My methodology involved recording the same meeting internally via a separate high-quality microphone and manually transcribing speaker turns to establish a ground truth. I then compared this to MeetGeek's automated output. The discrepancies were not minor.
The primary failure modes I observed are as follows:
* **Speaker Swapping:** This is the most frequent error. Participant A will speak for several sentences, but the diarization engine will assign the final one or two sentences to Participant B, despite no audible pause or change in acoustic characteristics sufficient for a human listener to perceive a switch. This corrupts the attribution of action items and technical points.
* **Failure to Distinguish Similar Voices:** In meetings with multiple participants of the same gender, the system frequently conflates them into a single speaker label after the first 10-15 minutes, effectively reducing a 4-person conversation to a 2-person dialogue in the transcript.
* **Inconsistent Label Persistence:** The label assigned to a specific speaker in one meeting is not reliably applied to the same speaker in a subsequent meeting, even when the audio setup is identical. This makes longitudinal analysis of individual contributions across a project lifecycle impossible without manual correction.
A representative sample of the output illustrates the issue. The following is an excerpt from a meeting on database load testing:
```json
{
"timestamp": "00:07:23",
"speaker": "Speaker 2",
"text": "We need to run the benchmark for at least two hours to observe cache warming effects."
},
{
"timestamp": "00:07:31",
"speaker": "Speaker 4", // Incorrect. This is still Speaker 2.
"text": "The variance in p99 latency should stabilize after the initial 30-minute ramp-up period."
}
```
The cost of this inaccuracy is non-trivial. It necessitates a complete manual review of any transcript before its data can be used for generating summaries of individual contributions or for performance retrospection. This negates a significant portion of the promised efficiency gain.
I am interested to know if other users with a methodological approach to tool evaluation have performed similar tests. Have you quantified the diarization error rate? Are there specific meeting conditions (e.g., number of participants, audio codec, presence of background noise) under which you have found it to be more or less accurate? Without substantial improvement in this core feature, the utility of the platform for technical and analytical teams remains severely limited.