Getting the best speaker labels
Speaker labels are only as good as the audio they're built from. A few habits at recording time make a bigger difference than anything you can fix afterward — and knowing what to clean up after the fact covers the rest.
Last updated 2026-08-31While you're recording
Why "phone in the middle" works
A phone's built-in microphone is small and unremarkable, but it's good enough for this — the thing that actually matters is position, not the hardware. Centering it means every voice reaches it at roughly the same level, which is exactly what a speaker-labeling model needs to tell voices apart. For a table of 2–6 people in a normal room, phone-in-the-middle is the whole trick.
When dedicated mics help
Once a room gets big, loud, or echo-y — or you're recording something more like a podcast than a meeting — a single centered phone starts to strain. Wireless podcast-style kits (the small clip-on mic + receiver setups you see on YouTube, from DJI, Rode, and similar) give each speaker their own close mic, which sidesteps distance and room-echo problems entirely and makes overlapping speech far easier to separate. They're overkill for a two-person call at a kitchen table, but worth it for a panel, an interview in an unfamiliar space, or anywhere crosstalk is unavoidable.
Overlapping speech is hard for any tool
Be honest with yourself about this one: no speaker-labeling system, including LymeScribe's, reliably untangles multiple people talking at once. When two voices genuinely overlap, the transcript may attribute the overlap to the wrong speaker or merge it into one line. The fix is upstream — turn-taking habits and mic placement — not downstream. A transcript from a well-behaved conversation will always beat post-processing tricks applied to a messy recording.
Cleaning up after transcription
- Name speakers. The labels come out as
Speaker 1,Speaker 2, and so on — LymeScribe plays a short clip of each voice, you type the name, and the whole transcript updates. Do this once per person and it applies everywhere they speak. - Corrections dictionary. If a name, term, or phrase keeps coming out wrong across your transcripts — a colleague's name, a product name, an acronym your team uses — add it to your corrections dictionary once and it's applied automatically on future transcripts, no repeated manual fixing.
Between good recording habits and these two cleanup steps, most transcripts need little to no manual correction by the time you're ready to hand them to an AI action — see prompt recipes for transcripts for what to do with the result.