Modern speech-to-text services advertise support for 100+ languages, but raw language count tells only part of the story. This is part of our guide to speech-to-text fundamentals. If you've ever wondered whether multilingual transcription actually works for your language—and what accuracy you can realistically expect—this article breaks down the current state of multilingual STT technology.
The Reality of Multilingual Accuracy
The gap between marketing claims and real-world performance can be significant. While leading models like OpenAI Whisper and ElevenLabs Scribe claim support for 99+ languages, accuracy varies dramatically based on how well-resourced each language is in training data.
High-Resource Languages
Languages like English, Spanish, French, German, and Mandarin benefit from vast amounts of training data. For these languages, modern STT achieves impressive results:
- Word Error Rate (WER): 3-8% under optimal conditions
- Real-world accuracy: 90-96% in clear audio
- Speaker diarization: Works reliably
- Dialect handling: Generally robust
For example, ElevenLabs Scribe reports 96.7% accuracy for English transcription, while Whisper Large-v3 achieves approximately 2.7% WER on clean audio.
Medium-Resource Languages
Languages like Portuguese, Italian, Dutch, Polish, and Hindi fall into a middle tier with moderate training data availability:
- WER: 8-15% in typical conditions
- Real-world accuracy: 85-92%
- Variability: Performance depends heavily on audio quality and accent
Deepgram's Nova-2 benchmarks show Hindi transcription improved by 41% over competitors, highlighting that specific model training makes a significant difference even for medium-resource languages.
Low-Resource Languages
Many of the world's 7,000+ languages fall into this category, including regional languages, indigenous languages, and dialects. The challenges here are substantial:
- WER: 15-50%+ depending on the language
- Real-world accuracy: 50-85% (often lower)
- Limited vocabulary: Rare words frequently missed
- Minimal improvement: Less competition driving innovation
Research shows that Whisper's average multilingual WER is approximately three times higher than its English WER, with performance ranging from 4.3% to 55.7% WER across different languages in benchmark tests.
Why Some Languages Work Better Than Others
Training Data Availability
The single biggest factor is the amount of labeled training data. English dominates because there are millions of hours of transcribed audio available for training. Languages spoken by smaller populations or in regions with less digital infrastructure have far less data.
Linguistic Complexity
Some languages present inherent challenges for speech recognition:
- Tonal languages (Mandarin, Vietnamese, Thai): Pitch carries meaning, requiring more sophisticated models
- Agglutinative languages (Turkish, Finnish, Hungarian): Long compound words create vocabulary challenges
- Languages without standardized spelling: Inconsistent orthography complicates transcription
Dialectal Variation
Even well-supported languages struggle with regional dialects. Arabic, for instance, varies significantly between Egypt, Lebanon, Morocco, and the Gulf states. A model trained primarily on Modern Standard Arabic may perform poorly on dialectal speech.
Code-Switching: A Special Challenge
Many multilingual speakers switch between languages mid-sentence—a phenomenon called code-switching. Traditional speech-to-text systems handled this poorly, often producing garbled transcripts when speakers mixed languages.
Recent advances have improved this significantly. AssemblyAI's Universal-1 model now handles code-switching across English, Spanish, French, German, Italian, and Portuguese in real-time, transcribing mixed-language speech in a single pass without requiring language switching.
However, code-switching support remains limited to major language pairs. If you're mixing, say, English and Tagalog, expect degraded accuracy.
What This Means for Your Workflow
Before Choosing a Tool
- Test with your actual audio: Benchmark accuracy doesn't predict real-world performance for your specific use case
- Check language-specific accuracy: Don't trust "100+ languages supported" claims at face value
- Consider audio quality: Contact center audio (8kHz) typically degrades accuracy by 15-30% compared to studio-quality recordings
Setting Realistic Expectations
| Language Tier | Expected Accuracy | Post-Editing Needed |
|---|---|---|
| High-resource | 90-96% | Light |
| Medium-resource | 85-92% | Moderate |
| Low-resource | 50-85% | Heavy |
Practical Tips
- Use automatic language detection carefully: Accented English sometimes triggers false Spanish detection
- For critical transcripts: Consider human review for low-resource languages
- Test speaker diarization: Not all languages have equally good speaker identification
Getting Started with Multilingual Transcription
If you work with audio in multiple languages, the key is matching your expectations to reality. High-resource languages like English, Spanish, and French offer near-human accuracy with modern tools. Low-resource languages require more careful quality control.
Scriby supports transcription in 100+ languages with straightforward pay-as-you-go pricing—no subscriptions or commitments. For languages where automated accuracy isn't sufficient, the platform makes it easy to review and correct transcripts alongside the original audio.
The multilingual speech-to-text landscape continues improving rapidly, with new models addressing previously underserved languages. But for now, knowing where each language stands helps you plan your workflow accordingly.