How Accurate Is Speech-to-Text? Real Numbers and What to Expect

Modern speech-to-text technology has reached remarkable accuracy levels, but what do the numbers actually mean? Whether you're transcribing interviews, meetings, or podcasts, understanding accuracy metrics helps you choose the right tool and set realistic expectations.

How Accuracy Is Measured: Word Error Rate (WER)

The industry standard for measuring transcription accuracy is Word Error Rate (WER). This metric calculates the percentage of incorrect words in a transcript by counting three types of errors: substitutions (wrong words), deletions (missing words), and insertions (extra words).

A 5% WER means 95% accuracy—roughly 5 errors per 100 words. Here's what different WER levels mean in practice:

• Under 5% WER: Professional quality, minimal editing needed
• 5-10% WER: Good quality, ready for most uses
• 10-20% WER: Acceptable but requires review
• Over 20% WER: Poor quality, significant manual cleanup needed

The difference between 85% and 95% accuracy might seem small, but it's substantial in practice. An 85% accurate transcript has roughly 15 errors per 100 words, making it difficult to read without heavy editing. At 95%, you're down to just 5 errors—often minor issues that don't impede understanding.

Current Accuracy Benchmarks (2026)

Today's leading speech-to-text systems achieve impressive accuracy under optimal conditions:

Top-tier performance:

• OpenAI Whisper Large-v3: 2.7% WER on clean audio
• GPT-4o Transcribe: Leading accuracy across benchmarks
• Google Cloud Chirp: 11.6% WER with 125+ language support
• AssemblyAI Universal-2: 14.5% WER for streaming transcription

Human baseline for comparison: Professional human transcribers typically achieve 4-6.8% WER, meaning the best AI models now match or approach human accuracy on clean recordings.

However, these benchmarks come with an important caveat: they're measured on curated test datasets. Real-world accuracy often differs significantly.

What Actually Affects Your Transcription Accuracy

Audio Quality

This is the single biggest factor. The same transcription engine can produce wildly different results depending on recording conditions:

• Studio-quality recording with a good microphone: 92%+ accuracy
• Conference room with moderate background noise: 78% accuracy
• Mobile phone call with background noise: 65% accuracy

Using a dedicated USB microphone instead of a built-in laptop mic can improve accuracy by 10-15% on its own.

Background Noise

Even moderate ambient noise—traffic, air conditioning, office chatter—causes transcription errors. Here's the counterintuitive part: applying noise-reduction software before transcription often reduces accuracy rather than improving it. The audio artifacts introduced by noise reduction can confuse speech recognition models.

For best results, record in a quiet environment rather than trying to fix noisy audio afterward.

Accents and Speech Patterns

Most speech-to-text models are trained primarily on standard accents, which means accuracy drops for regional dialects and non-native speakers. Studies show accuracy can decrease by 15-30% for speakers with strong accents compared to standard pronunciations.

Choosing the correct language variant matters. Switching from generic English (en-US) to a regional variant can dramatically improve results—in some cases reducing WER from 37% to around 10%.

Speaker Overlap

When multiple people talk simultaneously, accuracy plummets. Most transcription systems struggle to separate overlapping voices, resulting in garbled text or one speaker being dropped entirely. Speaker diarization (identifying who said what) helps organize transcripts but doesn't solve the fundamental overlap problem.

Domain-Specific Vocabulary

Speech-to-text models have large vocabularies, but they don't know your company's product names, industry jargon, or technical terms. When the model encounters an unfamiliar word, it substitutes something that sounds similar—often incorrectly.

Many services offer custom vocabulary features to address this. Adding your specific terms can improve accuracy by 5-15% for domain-heavy content.

Setting Realistic Expectations

Here's what to expect in typical scenarios:

The key insight: accuracy depends more on your audio conditions than which transcription tool you choose. A mid-tier service with clean audio will outperform a premium service with poor audio every time.

Practical Tips to Improve Your Results

1. Invest in audio quality first. A $50 USB microphone makes more difference than switching transcription providers.

2. Record in quiet environments. Reduce background noise at the source rather than in post-processing.

3. Use speaker diarization. It won't improve word accuracy, but it makes transcripts much more usable by identifying who said what.

4. Add custom vocabulary. If your content includes specialized terms, take advantage of custom vocabulary features.

5. Plan for review time. Even at 95% accuracy, a 30-minute recording will have 50-100 errors. Budget time for a quick review.

Conclusion

Speech-to-text accuracy has reached the point where AI transcription is genuinely useful for most applications. Leading systems achieve 90-99% accuracy under good conditions—comparable to human transcribers.

But accuracy isn't just about the software. Your recording quality, audio environment, and content type matter just as much. Focus on clean audio, realistic expectations, and a quick review process, and you'll get transcripts that actually serve your needs.

If you're looking for a straightforward way to transcribe audio without subscriptions or complex setups, Scriby offers pay-as-you-go transcription with speaker diarization. Upload your file, get your transcript, and pay only for what you use.