"99% accurate!" claims are everywhere and mostly measured on ideal audio. Here's what accuracy actually means, what breaks it, what to realistically expect from your recording — and how to record so the transcript comes out nearly perfect.
The industry metric is word error rate (WER). Take the machine transcript, compare it to what was actually said, and count three kinds of mistakes: substitutions (wrong word), insertions (extra word), and deletions (missed word). Divide the total by the number of words spoken, and that's your WER. A WER of 5% means roughly one word in twenty is wrong — often quoted the other way round as "95% accurate."
Two things people miss about WER: first, it treats all errors equally, but in practice a botched name, dosage or amount hurts far more than a mangled "um, well". Second, published WER numbers come from benchmark datasets — often clean, single-speaker, native-accent audio. Your real-world number depends almost entirely on your recording.
The single biggest factor. Traffic, café chatter, air conditioning, keyboard clatter and wind all compete with the voice. Modern models are trained on noisy audio and cope surprisingly well with steady background hum, but loud or speech-like noise (a TV, other conversations) directly corrupts words.
Closely related to noise: a speaker two metres from a laptop mic sounds reverberant and quiet, and accuracy drops sharply. A phone lying near the speaker beats an expensive mic across the room.
Models like Whisper are trained on hundreds of thousands of hours of multilingual audio, so common accents transcribe well. Very heavy regional accents, dialects and strongly non-native pronunciation still raise error rates — the model substitutes the nearest word it "expects".
Specialist vocabulary — medical terms, product names, people's names, acronyms — is where errors cluster, because the model may never have seen the word. It will confidently write a plausible-sounding replacement, which is exactly why proofreading matters.
Turn-taking conversation is fine. People talking over each other is not — overlapping speech is genuinely ambiguous audio, and every transcription system struggles with it. Panel discussions and heated meetings are the hardest common scenario.
Heavily compressed audio (low-bitrate voice notes, phone calls, old recordings) throws away exactly the frequencies that distinguish similar consonants. Clipping — recording so loud the waveform distorts — is even worse.
| Scenario | Typical result |
|---|---|
| Podcast / voice-over, good mic, one speaker | Excellent — usually 97–99% of words right; near-publishable |
| Zoom/Teams meeting, everyone on headsets | Very good — expect a handful of fixes per page, mostly names |
| Interview, phone on the table in a quiet room | Good — solid transcript, check names, numbers and quiet moments |
| Lecture, speaker far from the recorder | Usable — main content lands; reverberant sections degrade |
| Group conversation with crosstalk, café noise | Rough — expect real gaps and mixed-up speakers; skim, don't quote |
Even a 97%-accurate transcript of a 1,500-word interview contains ~45 wrong words — and they're disproportionately the names, figures and technical terms you care about. The point of AI transcription isn't zero errors; it's that fixing 45 words takes five minutes, while typing 1,500 words with timestamps takes over an hour. The timing, structure and 95%+ of the words are already done for you.
The honest way to answer "how accurate is it for me?" is to test with your real recordings. ScribeGrab runs Whisper large-v3 — one of the most accurate open speech-recognition models — free, with no daily cap and no sign-up, and gives you the transcript plus SRT and VTT subtitles from one upload. Your file is deleted right after processing.
On clear, well-recorded speech, modern models typically get 95–98% of words right (WER roughly 2–5%). Noise, heavy accents, crosstalk, jargon and poor microphones pull that down — noisy multi-speaker audio can fall well below 90%.
The standard accuracy metric: substitutions + insertions + deletions, divided by words spoken. 5% WER ≈ 1 wrong word in 20. Lower is better.
Mic close, quiet room, one voice at a time, healthy levels without clipping, original files instead of re-compressed ones, and set the language manually on very short clips.
Yes — errors cluster on names, numbers and jargon, exactly the words that matter. But fixing a few dozen words beats typing the whole thing by an hour or more.