The analogy

The accuracy of a transcription is like the sharpness of a photo. You can have the best camera in the world, but if you shoot in the dark, with a shaky hand and three people moving around, the picture comes out blurred all the same. And if you shoot in full sunlight, on a tripod, even an average camera gives you a sharp shot.

The audio is the light. A single voice, a close microphone, a quiet room: even an ordinary tool transcribes well. Three people talking over each other in a room with an echo: no tool will save you. That's why the question "which one is most accurate" is only half the question. The other half is "under which conditions," and that's the half that decides the result.

How it really works

What accuracy actually measures

The technical figure is called word error rate: the percentage of words that are wrong, missed or added compared to a perfect transcription. The lower, the better. The point is that the same tool has very different rates depending on the audio: OpenAI's engine drops to a few percentage points on clean English audio, but climbs to double digits on real, noisy audio.

The different philosophies of the tools

The tools don't compete only on decimal points of accuracy. They split by philosophy. Some aim for maximum raw accuracy, leaving you the job of fixing the text: they win on benchmarks but hand you a block of text to polish. Others aim for ready-to-use meeting transcription, with speaker recognition, summaries and action items: they sacrifice a few decimals of accuracy to give you an already organized result. Others still favor multilingual support and languages other than English, where the first group often falters.

The right choice isn't "the most accurate in absolute terms," but "the most suited to what I need to do with it": a perfect raw text to edit, or a ready-made set of minutes.

The factor that weighs more than the tool

The brochure numbers (95-99%) hold under ideal conditions: a native speaker, a professional microphone, silence. In real meetings accuracy is typically much lower. That means improving the audio beforehand — a close microphone, a quiet environment, one person at a time — shifts the result more than any change of tool. The bottleneck is the recording, not the software.

What you can do in practice

  • Start with the language. For Italian and languages other than English, test the tool on your own audio before trusting it with a long job: top records on English don't automatically carry over.
  • Choose based on the use, not the benchmark. If you need meeting minutes with who-said-what, get a tool designed for meetings. If you need text to polish by hand (an interview to publish), get the one with the highest raw accuracy.
  • Take care of the audio first. A microphone close to the speaker, a quiet environment, avoiding overlaps. This is the intervention that improves transcription the most, at zero cost.
  • Always verify the parts that matter. Proper names, numbers, figures, technical terms: these are what transcriptions get wrong most often. Reread those passages even on a transcription that's "98% accurate."
  • If you can't find a feature's button, look in the service for entries like "speaker identification," "speaker recognition" or "language": the names change, the functions stay. When in doubt, start with a short test.

A common misconception

"There's a most accurate tool out there; you just have to find it and always use that one." Accuracy isn't a fixed property of the tool: it's the result of the encounter between tool and audio. The same software that makes 3% errors on a clean voice makes three times that on a crowded meeting. Chasing the winner of the tests while ignoring recording quality is the most common way to end up disappointed. Accuracy is earned before the microphone, not after.

Frequently asked questions

For Italian, which is the best?

Test it yourself, on your own type of audio. The strongest tools on English don't always keep the same level on other languages, and accent, your industry's jargon and recording quality change the result a lot. A five-minute test tells you more than any ranking.

Are automatic transcriptions already good enough for legal or medical work?

For a draft yes, for the final document no, not without human review. The very details that matter in those fields — names, dosages, figures, technical terms — are the ones the tools get wrong most. Use the transcription as a basis and have the critical parts verified by a competent person.

Does a transcription that's "99% accurate" mean I can trust it without rereading?

No, and it's the number that misleads the most. The 99% refers to ideal conditions and to an average: the 1% of error can fall right on the name or the figure that matters. The high percentage reduces the review work, it doesn't eliminate it. The decisive passages get reread anyway.