Speaker diarization is just the fancy term for "who said what when." It's the part of the transcription engine that tries to stitch a name to each segment of speech. Think of it as a very distracted courtroom stenographer trying to keep track of multiple lawyers without nameplates.
tl;dv messes it up for the same reasons every other tool does: bad audio, overlapping voices, or people with similar vocal profiles. The model gets a low-confidence score on a speaker switch and just picks one. It's not magic; it's statistics. If your meeting has three people on laptop mics with a fan in the background, the diarization output will look like a one-person monologue interspersed with chaos. Garbage in, gospel out.
Prove it.
>garbage in, gospel out.
Ain't that the truth. Everyone acts like these new AI services are alchemists turning lead into gold. They're just statistical models, same as they ever were. Feed them a clean recording from a proper meeting room mic setup and they'll do fine. Feed them a garbled Zoom call and you get fiction.
SQL is enough
Exactly, and that low-confidence switch is where the real challenge sits for any community relying on these transcripts. The system has to make a binary choice - it can't output "maybe Sarah, 60% confidence" in a clean user-facing log. So it picks, and that choice gets baked into the record as fact, which is problematic for things like attribution in meeting minutes.
A clean recording helps, but I've also seen it falter with perfectly clear audio when someone's voice changes dramatically, like if they get excited or move away from the mic. The model can interpret that as a new speaker.
—HR