Speech-to-text technology has made remarkable progress, but accuracy isn't uniform across all speakers. If you've ever noticed your transcription struggling with certain voices, accents are likely the culprit. Research shows that accuracy can drop 20-40% for non-native accents depending on the tool—a gap that affects millions of users worldwide.
Understanding why this happens—and what you can do about it—helps you choose better tools and set realistic expectations.
Why Speech Recognition Struggles with Accents
Speech recognition systems learn by analyzing thousands of hours of recorded speech. The challenge is that most training datasets heavily favor certain accents, typically American English. When a system encounters speech patterns it hasn't seen much during training, accuracy suffers.
The Training Data Problem
Modern ASR (automatic speech recognition) models are only as good as the data they're trained on. Studies from the National Institute of Standards and Technology reveal that word error rates for non-native speakers can reach 28%, compared to 6-12% for native speakers of the dominant training accent.
This isn't a flaw in the technology itself—it's a reflection of which voices were included when building these systems. Virtual assistants like Siri and Alexa have faced well-documented issues with Scottish, Irish, and Indian accents, forcing many users to modify how they speak to be understood.
What Accents Actually Change
Accents alter the fundamental building blocks of speech—phonemes. Changes in vowel sounds, consonant pronunciation, syllable stress, and intonation create acoustic patterns that differ from what the system expects.
For example:
- Indian English often features retroflex consonants
- African English variants may use different syllable stress patterns
- Scottish English has distinct vowel sounds that differ from American pronunciation
When these patterns don't match the model's training data, the system makes more errors.
The Real-World Accuracy Gap
Research from Stanford University found that speech recognition systems have error rates 16-20% higher for non-native accents compared to standard native accents. A 2025 benchmark evaluating multiple ASR systems on non-native English speakers (with Chinese and Indian accents) showed significant performance variation between platforms.
The gap matters beyond individual frustration. In professional settings—medical transcription, legal proceedings, customer service—inaccurate transcription can have serious consequences. Studies document 2.8–5.7× degradation from controlled benchmark conditions to real-world environments.
Which Tools Handle Accents Better?
Not all speech-to-text tools perform equally with diverse accents:
- Whisper (OpenAI): Trained on 680,000 hours of multilingual data, it generally handles diverse accents better than many competitors
- Specialized models: Tools like PolyAI's Owl model, trained specifically on varied accents and phone-line audio, achieve remarkably low error rates in real-world conditions
- General cloud APIs: Standard offerings from major providers often struggle more with accented speech
The key differentiator is training data diversity. Tools built with intentionally varied voice samples perform better across different speaker backgrounds.
How to Improve Results with Accented Speech
If you regularly transcribe audio with diverse accents, several strategies can help:
Choose the Right Tool
Look for transcription services that explicitly mention multi-accent training or global language support. Some platforms let users select a region or accent preference, which adjusts the underlying model for better accuracy.
Audio Quality Matters More
Background noise compounds accent-related accuracy issues. Clean audio with minimal background interference gives the system the best chance of accurate transcription. For accented speech especially, investing in good recording quality pays dividends.
Review and Edit
No transcription tool is perfect with any accent. Building in time for human review—especially for important content—ensures accuracy where it matters most. Many professionals use AI transcription as a first draft, then refine the output.
Consider Speaker Diarization
When multiple speakers with different accents appear in the same recording, speaker diarization (identifying who said what) helps maintain clarity. Tools that offer this feature can separate speakers, making it easier to review and correct each person's segments.
The Path Forward
The speech recognition industry is actively working on accent inclusivity. Approaches include:
- Multi-accent training: Building systems that treat accents as natural variations rather than errors
- Accent embeddings: Technical approaches that help models recognize and adapt to regional speech patterns
- Larger, more diverse datasets: Expanding training data to include more global voices
Progress is happening, but it's gradual. In the meantime, understanding these limitations helps you work around them.
Getting Better Transcription Results
For professionals working with diverse audio sources, the accent accuracy gap is a practical reality to navigate. The best approach combines choosing tools with strong multi-accent performance, maintaining high audio quality, and planning for human review.
Scriby supports over 100 languages and leverages modern transcription models designed for diverse speech patterns. With pay-as-you-go pricing, you can test how well it handles your specific audio without commitment—upload a sample and see the results for yourself at scriby.app.