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"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.
We ran our own model against LibriSpeech test-clean — the standard read-English benchmark, which ships with official human transcripts — and measured a 1.94% word error rate: 98.1% of words right. The previous model scored 2.45% on the same material, so the current build is both more accurate and 3.9× faster on long recordings. Read English in a quiet studio is easier than the meetings, interviews and phone recordings people actually upload, so treat 98.1% as the ceiling, not the average.
The real-world numbers back that caveat up. In the 40 days to 21 August 2026, 63 transcription jobs failed outright rather than finishing — separate from the 1,083 uploads rejected before processing even started for being over a limit. Processing itself was measurably slow on 13 of those jobs, each logged at around 125 seconds against a typical run of seconds to a few minutes. None of that is the word-error-rate number moving; it's the difference between a clean benchmark file and whatever actually gets uploaded, which is exactly why the rest of this page is about what hurts accuracy in practice, not just what the lab number says.
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.
Modern speech-recognition models 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 automatic 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.
Some situations push error rates up no matter how good the underlying model is:
None of this is specific to any one transcription service, ours included: it's where the audio itself runs short of information, not a gap a bigger model closes on its own.
A quick proofread closes the gap for most everyday use. A few situations call for more than that:
For everything else, an automatic transcript plus a proofreading pass is faster and just as reliable.
The honest way to answer "how accurate is it for me?" is to test with your real recordings. ScribeGrab runs our speech model (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.
Code-switching between languages mid-sentence, singing and lyrics, strong dialects, live/real-time captioning, and heavily degraded phone audio all push error rates up regardless of the model. These are limits of the audio and the task, not something a specific tool fixes by being "more advanced".
Scan for names, numbers and jargon first, check the start and end of each paragraph, and play the audio at 1.5–2x speed while reading along. A misspelled name usually repeats the same way throughout, so fix it once and search for the rest.
For certified legal or medical transcripts, captions for deaf or hard-of-hearing viewers, or heavy crosstalk with several speakers. For everyday interviews, meetings and videos, an automatic transcript plus a quick proofread is faster and just as reliable.