How speaker diarization works — and where it slips
Splitting a recording by who is talking is a different problem from writing down what they said. Here is what the model actually listens for.
What the model actually listens for
A transcription model turns sound into words. Diarization answers a different question: who was speaking at each moment. The system cuts the audio into short segments, turns each one into a numeric fingerprint of the voice, and groups the fingerprints that resemble each other. Each group becomes a speaker label. Nothing in that process knows anyone's name — it only knows that this voice is not that voice.
Where it slips
Three situations account for most mistakes.
- Overlap. When two people talk at once, a segment carries two voices and lands in whichever group it resembles more.
- Short turns. A one-word "yeah" gives the model very little to fingerprint, and brief interjections are the first thing to be mislabelled.
- Similar voices. Two speakers of the same age, gender and accent, recorded through the same microphone, can collapse into a single label.
Recording conditions matter more than the model does. A meeting captured by one laptop microphone in a reverberant room is a harder problem than the same meeting recorded with each participant on their own channel.
What to do about it
Fixing labels afterwards is faster than fighting the recording. In WhoScribe you rename a speaker once and every line carries the new name.
Read the first minute closely. Greetings and introductions are where labels get swapped most often, and correcting them early means the rest of the transcript reads correctly.
If you know how many people are in the room, say so. A model told there are three voices will not invent a fourth out of background noise.
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