Updated September 2026
No transcription tool, human or AI, produces a perfect transcript from imperfect audio. Understanding what actually causes errors lets you fix the fixable ones before you upload a file, rather than editing them out afterward.
Background noise and low gain. A recording made on a phone sitting on a table across a room asks a lot of any transcription engine — the speech signal is competing with room echo, HVAC hum, and distance. A lavalier mic or a phone held closer to the speaker fixes more errors than any software setting.
Overlapping speech. When two people talk at once, even human transcribers guess. Speaker diarization (splitting a transcript by who is talking) also degrades sharply during overlap, because the model has to separate two waveforms that are genuinely mixed together, not just labeled differently.
Domain-specific vocabulary. Names, acronyms, and technical jargon that are rare in general speech are the words a model is least confident about, because it has seen them least often in training. A recording full of drug names, legal terms, or company-specific acronyms will have a higher error rate on exactly those words, even when the rest of the sentence is transcribed cleanly.
Accent and dialect variation. Modern models like Whisper large-v3 are trained on a broad multilingual, multi-accent dataset and handle this far better than older engines, but strong regional accents combined with fast speech still produce more substitution errors than clear, moderate-paced speech.
Record closer to the speaker than feels necessary — proximity beats a more expensive microphone in a noisy room. If you are interviewing multiple people, ask them not to talk over each other, or record on separate tracks and mix afterward. If a recording is full of jargon or names the transcript is likely to miss, keep a short list of the correct spellings so you can do a fast find-and-replace pass after transcription — this takes a few minutes and fixes the majority of remaining errors in a technical or specialized recording.
Finally, treat the first transcript as a draft, not a final document, any time the recording will be quoted directly or published. Automated transcription — from any provider — gets you from an hour of audio to a searchable, mostly-correct document in a couple of minutes. Getting from mostly-correct to publication-ready still takes a short human pass on anything you plan to quote word-for-word.