People blame the model when a transcript comes out mangled. Sometimes it is the model. More often the failure was baked in before a single word was recognized — in the room, the mic, the settings, or the tool's own limits. This list ranks the causes by how much they actually cost you, with the fix for each. We build QuickSpeak, an on-device transcriber, and the fixes are generic on purpose: they'll improve any tool you use.
1. The microphone is too far away
The single biggest factor, and the one no software fixes. A laptop mic across a conference table hears the first two rows well and everyone else as room noise; every downstream step — word accuracy, speaker separation — inherits that damage. Fix: sit within a couple of meters for in-person capture, use a real microphone for hybrid rooms, or record the browser tab for online calls, where the source is perfect by construction. Lecture halls are the extreme case: the back row needs the lecturer's mic, not your laptop.
2. People talk over each other
Crosstalk produces blended audio that no diarization model separates cleanly — this is physics, not a product gap. Turn-taking transcribes well; interruptions don't, everywhere, in every tool, at every price. Fix: none that's satisfying. Say "one at a time" like a moderator, and when a transcript garbles exactly the overlapping stretch, check the audio rather than blaming the model. Tools that keep the recording (as opposed to transcript-only capture) let you verify — one reason we refuse to discard audio.
3. Your jargon, names, and product terms aren't in its vocabulary
Generic models trained on general speech fumble exactly the words that matter most in your meetings: client names, internal codenames, industry terms. "Onnetscape" for "OnScape" isn't a hearing problem; it's a vocabulary problem. Fix: use custom vocabulary where your tool has it — QuickSpeak Pro lets you teach the canonical spelling once, with aliases cleaned up automatically. For recurring calls, teaching the five recurring names fixes eighty percent of the embarrassing errors.
4. The language was pinned wrong (or auto-detect guessed a neighbor)
Auto-detect listens to the first moments and picks one language per meeting. Norwegian's two written forms, Croatian and Serbian-adjacent vocabularies, Hindi versus Urdu-leaning audio — the guess can land one over. And a call that switches languages mid-session gets transcribed in whichever language was detected first. Fix: for known single-language calls, pin the language manually; reserve auto-detect for mixed weeks. QuickSpeak's selector covers 36 languages, and the same advice applies to any tool's selector.
5. Noise, speakerphone, and compression
Speakerphone echoing a room, Bluetooth headsets dropping syllables, and low-bitrate conference audio strip the details models rely on. This is factor one (distance) wearing a disguise: the mic hears garbage and faithfully transcribes it. Fix: wired or good wireless headsets beat speakerphone; noise suppression (Krisp's specialty, built into many conferencing apps) helps the humans and the model at once; and if the recording sounds bad to you, it reads worse to the model.
6. Your tool meters, caps, or truncates
The sneakiest cause, because the transcript looks fine — until you notice it stops. Free plans cut conversations mid-sentence: Otter's free tier caps at 30 minutes per conversation, so the last fifteen minutes of a 45-minute client call never existed. Storage caps (Fireflies' 400-minute hard stop) make new meetings unviewable until you delete old ones. Fix: check whether the ending of every transcript is a natural stopping point, and if your tool is rationing you, switch to one that isn't — unlimited free transcription exists, for structural reasons worth knowing.
The 60-second audit
Next time a transcript disappoints, check in this order: How far was the mic from the speaker? Was anyone talking over anyone? Do the errors cluster on names? Was the language pinned? Does the recording sound compressed? Does the transcript end abruptly? Five out of six times, the fix is in that list — not in a different model.