If you speak English as a second language, you've probably noticed that speech-to-text tools don't always understand you as well as they understand native speakers. You're not imagining it—research shows accuracy can drop 15-30% for non-native accents, with word error rates sometimes doubling compared to American English speakers.
This is part of our guide to choosing the right speech-to-text tool. Here, we focus specifically on which tools work best for speakers with accents and what features to look for.
Why Accuracy Drops for Non-Native Speakers
Most speech recognition systems are trained primarily on American English or British Received Pronunciation. When your speech patterns differ from this training data—whether due to accent, pronunciation habits, or language transfer from your native tongue—the system makes more mistakes.
The core issue is training data bias. ASR engines trained on limited datasets don't include enough variety of English accents. A speaker from India, Germany, or Brazil brings phonological patterns from their native language that these systems weren't designed to handle.
The good news: this is improving. Industry benchmarks show non-native accent recognition has improved from around 35% word error rate to approximately 15% WER in recent years. But tool selection still matters significantly.
What to Look for in a Transcription Tool
When evaluating speech-to-text tools as a non-native speaker, prioritize these features:
Diverse training data - Tools trained on speech from multiple countries and accent groups perform better. Look for providers that explicitly mention international or multilingual training.
Language and accent customization - Some tools let you specify your language background or accent type, helping the model adjust its expectations.
Custom vocabulary support - If you work in a specialized field, the ability to add custom terms helps compensate for pronunciation variations on technical words.
Post-processing capabilities - Some platforms use AI post-processing to clean up transcripts after initial recognition, which can recover accuracy lost to accent-related errors.
Top Tools for Non-Native English Speakers
Based on testing and industry research, these tools handle accented English better than average:
OpenAI Whisper
Whisper consistently performs well with international accents due to its diverse training dataset. It was trained on 680,000 hours of multilingual audio, giving it exposure to many English accent variations. Available through various interfaces and completely free for local use.
Best for: Users comfortable with technical setup who want free, high-accuracy transcription.
Speechmatics
Specifically designed for global use, Speechmatics was trained using speech from 40 countries. The company explicitly focuses on accent handling as a core feature, making it particularly strong for international business contexts where multiple accent types appear in the same recording.
Best for: Enterprise users with international teams and mixed-accent meetings.
AssemblyAI Universal
AssemblyAI's Universal model supports 99 languages at a flat rate with no premium charges for non-English transcription. Their system allows you to customize detectable languages for better accuracy with heavy-accented audio. Independent benchmarks show it handles accented speech effectively.
Best for: Developers and businesses needing API access with consistent multilingual performance.
Deepgram Nova-2
Deepgram's Nova-2 includes multilingual code-switching support, useful if you naturally mix languages while speaking English. The model handles dynamic, multilingual settings well, though performance varies by specific language background.
Best for: Users who code-switch between languages or work in multilingual environments.
Notta
Notta excels with strong accents and international team meetings. It offers real-time translation across 58 languages and handles accent variations better than many competitors, making it practical for global teams.
Best for: Teams with international members who need meeting transcription with translation.
Tools That Struggle with Accents
Not every popular transcription tool handles accents well. Some to approach with caution:
Tools optimized for American English - Many consumer-focused apps are trained primarily on American accents and struggle noticeably with other varieties of English.
Older generation models - Speech recognition has improved dramatically in recent years. Tools using older models may have significantly higher error rates for non-native speakers.
Budget options with limited training data - Free tools often cut corners on training data diversity, leading to worse performance for anyone who doesn't sound like their primary user base.
Tips for Better Results
Regardless of which tool you choose, these practices help improve accuracy:
- Use a quality microphone - Clear audio compensates for accent-related challenges
- Speak at a moderate pace - Rushing increases recognition errors for everyone, but especially for accented speech
- Minimize background noise - Accent and noise compound each other's negative effects on accuracy
- Review and correct consistently - Many tools learn from corrections over time, improving accuracy for your specific voice
Finding the Right Fit
The best tool depends on your specific situation. Consider your accent strength, whether you work in multiple languages, your budget, and whether you need real-time or batch transcription.
For most non-native speakers, starting with Whisper (free) or a trial of AssemblyAI or Speechmatics gives you a good baseline for comparison. Pay attention to how well each handles your specific accent and use case rather than relying solely on general accuracy claims.
If you're looking for a straightforward option that supports 100+ languages with pay-as-you-go pricing, Scriby uses modern speech recognition models designed to handle diverse accents and speaking patterns—worth trying if you want simple transcription without subscription commitments.