How Accurate Is AI Transcription? (And How to Get Cleaner Results)

The short answer

On clear audio with a good microphone, modern AI transcription reaches roughly 85–95% accuracy. With a quiet room, a close mic, and one speaker at a time, it can climb above 95%. With heavy background noise, crosstalk, or strong accents, accuracy drops — sometimes well below 80%.

So the honest answer is: accuracy is not a single number. It depends far more on the audio you feed the model than on the model itself.

What "accuracy" actually means

The industry measures transcription quality with Word Error Rate (WER) — the percentage of words that are wrong, missing, or inserted compared to a perfect human transcript. A 10% WER means roughly 1 in 10 words needs a fix; that's about 90% accuracy.

WER counts three kinds of mistakes:

• Substitutions — the wrong word ("their" for "there").
• Deletions — a word the model missed entirely.
• Insertions — a word the model added that wasn't said.

A clean transcript with a few punctuation quirks can still have a very low WER. A transcript that drops whole sentences during crosstalk will have a high one.

What lowers AI transcription accuracy

Most accuracy problems trace back to a handful of audio issues:

• Background noise — fans, traffic, cafés, and HVAC hum all compete with speech.
• Distance from the mic — every extra meter loses clarity. A phone on the table beats a laptop across the room.
• Overlapping speakers — when two people talk at once, the model has to guess.
• Accents and dialects — strong or less-common accents raise WER, though good models keep improving here.
• Jargon, names, and acronyms — product names, medical terms, and people's names are the most common errors.
• Low-quality recordings — compressed phone calls and old voice memos carry less detail for the model to work with.

How to improve accuracy

You can move accuracy up sharply without any special equipment:

1. Get the mic closer. Place your phone within arm's reach of whoever is speaking. This is the single biggest lever.
2. Reduce background noise. Close windows, mute notifications, and move away from fans or busy hallways.
3. Encourage one speaker at a time. Crosstalk is where most words get lost — a quick "let's go one at a time" helps the transcript as much as the meeting.
4. Record at the source. Recording in person or directly from the device beats re-recording audio played through a speaker.
5. Use speaker labels. Diarization that separates each voice makes the transcript easier to read and to correct.

How Soria handles accuracy

Soria transcribes recordings and uploads with automatic punctuation and multi-speaker diarization, so you can see who said what. It supports 30+ languages with real-time translation, and turns the raw transcript into a summary and action items — so even when a few words need fixing, the meaning is captured.

The takeaway: AI transcription is already accurate enough to replace typing for most meetings, lectures, and interviews. Feed it clean audio, and noticeably higher accuracy is well within reach.

Quick answers

• How accurate is AI transcription? Typically 85–95% on clear audio, higher with a close mic and low noise.
• What is WER? Word Error Rate — the share of words that are wrong, missing, or added versus a perfect transcript.
• Why are names spelled wrong? Proper nouns, jargon, and acronyms are the hardest words for any model; a quick edit fixes them.
• Can I improve accuracy? Yes — closer mic, quieter room, and one speaker at a time make the biggest difference.