Why do transcripts get speakers wrong?

Overlapping speech, similar voices, poor microphones and several participants sharing one room are all hard cases. Accuracy usually drops sharply when a group shares a single device.

Does it matter if the words are accurate anyway?

Yes, for anything that extracts actions. An unattributed commitment cannot be assigned, so the summary degrades into notes nobody owns.

Diarization

Diarization is working out who spoke when in a recording, so a transcript reads as an attributed conversation rather than one undifferentiated block of text.

Transcription and diarization are separate problems solved by separate models. Transcription turns audio into words. Diarization segments the audio by voice and groups those segments into speakers. A system can be excellent at one and poor at the other, which is why a transcript can be word-accurate and still useless for working out who committed to what.

Attribution is what makes a meeting transcript actionable. "We will send the revised numbers by Friday" is a fact about a meeting; "the customer’s finance lead will send the revised numbers by Friday" is something you can act on. Anything downstream that extracts owners, commitments or follow-ups depends on the diarization being right.

Frequently Asked Questions

No. Diarization separates voices and labels them consistently within one recording. Speaker recognition identifies whose voice it is, which requires a reference sample for that person.