How accurate is AI transcription?

Accuracy is measured industry-wide as Word Error Rate (WER) — the sum of inserted, substituted, and deleted words divided by total words in the reference transcript. A WER of 5% means 5% of words are wrong, so 95% accurate.

Real-world benchmarks (2026): - Clean studio audio, single speaker, standard accent: WER 2-5% (95-98% accurate). - Podcast recording, good mics, 2-3 speakers: WER 5-8%. - Meeting recording, average audio, overlapping voices: WER 10-15%. - Phone call, lossy compression, regional accent: WER 15-25%. - Live event, heavy background noise, shouting: WER 30%+.

Human transcribers reach 2-4% WER on optimal audio but drop to 10-15% on bad recordings — AI is now competitive in that range.

What pulls accuracy down: - Background noise (HVAC, traffic, reverb) - Overlapping speakers (two people talking at once) - Strong regional accents without dialect-specific training (Swiss German is notoriously hard) - Domain jargon, proper nouns, product names — without a custom vocabulary, the model nails "aspirin" but mangles "pembrolizumab" or your customer's name. - Very fast, mumbled, or whispered speech.

What lifts accuracy: - Good mics (an XLR mic over a laptop mic moves WER noticeably). - A custom vocabulary with domain terms and proper nouns. - The right model: DeepScript Premium uses a larger, slower model with DACH dialect tuning (CH/AT/DE) and delivers around 30% fewer errors than Standard on Austrian and Swiss German audio. - Human post-editing for stakes like court records or published captions.

Rule of thumb: if a vendor claims "99% accuracy" without naming the test set, that number is marketing. Trustworthy providers cite WER on public benchmarks (Common Voice, LibriSpeech, FLEURS), not a polished marketing percentage.