How accurate is AI transcription, and what affects it?

Good, and better than it was

Modern AI transcription is at the point where clean, clearly-spoken audio comes back with only the odd error, and the models keep improving. A one-on-one call with good microphones, or an online meeting where everyone takes turns, will usually transcribe well enough to use with a quick read-through.

It is not perfect, and it never will be, because some of what hurts accuracy is in the audio rather than the model. Knowing what those things are tells you where a transcript is likely to slip, and what you can do about it.

What trips it up

A few things reliably raise the error rate.

• Poor audio. Background noise, a distant or low-quality microphone, and echo all make speech harder to make out, for a model just as for a person.
• Overlapping speech. When two people talk at once, the model has to guess at a tangle. This is the single most common source of garbled lines in meeting transcripts.
• Accents and unfamiliar voices. Models handle common accents well and struggle more with ones they have seen less of.
• Jargon, names and acronyms. Everyday words come back reliably; specialist terms, product names and people’s names are where errors cluster.

What actually improves accuracy

The biggest lever is the audio you feed in, not any setting. A decent microphone, a quiet room, and one person speaking at a time will do more for a transcript than switching models. If you are recording a call, recording each speaker on a separate track helps too, because the model is transcribing one clear voice at a time instead of untangling overlapping ones.

After that, a quick read-through to fix the names and jargon is usually all a good transcript needs. Those errors cluster in predictable places, so correcting them is fast once you know to look.

On-device versus the cloud

There used to be a real accuracy gap between transcribing on your own machine and sending audio to a cloud service. For everyday meetings, that gap has largely closed. Current on-device models are strong, so for most audio the choice between local and cloud is now about privacy rather than quality. If your machine can run a good model locally, you are not trading much away to keep the audio on it.