How accurate is AI translation in real production environments? Let’s deep-dive into the foundation Voice Translation v3 is built on: accuracy.
In the conversations Voice Translation (VT) is built for, a wrong medication name, a misheard policy number, or a mistranslated disclosure carries real consequences. Accuracy at this level isn’t a quality metric. It’s what the entire system depends on.
Learn about AI voice translation for call centers.
AI translation accuracy: evaluation results
Krisp Voice Translation has been evaluated across 30 languages, 6 business domains, and 870 conversations, using three independent validation layers: automated benchmarking, AI-driven semantic scoring, and bilingual human review.
| Metric |
Result |
| English transcription accuracy (WER) |
~2.7% (97 out of 100 words correct) |
| Target language transcription accuracy |
2–10% WER for most languages |
| Translation quality (BLEU), top languages |
51–66 (human translations typically score ~60) |
| Semantic accuracy (Accuracy QA) |
94–96 / 100 across all benchmarked languages |
Proven in practice: healthcare deployment
The strongest evidence comes from a live deployment at a national healthcare services provider supporting public health programs that serve millions of consumers. Voice Translation handled complex patient conversations across 8 languages: medical terminology, prescription names, patient identifiers, dates of birth.
| Metric |
Result |
| Multilingual calls completed end-to-end |
90% (no interpreter needed) |
| Overall translation accuracy (Accuracy QA) |
96% |
| Patient safety incidents |
Zero |
| Languages in one workforce |
8+ |
| Interpreter wait time |
0 seconds |
Accuracy by language:
| Language |
Score |
|
Language |
Score |
| Spanish (US) |
96% |
|
Russian |
98% |
| Spanish |
97% |
|
Vietnamese |
96% |
| English (US) |
97% |
|
Hindi |
97% |
| Arabic |
97% |
|
Korean |
94% |
Read the full Voice Translation v3 announcement
Voice Translation language quality tiers
To rank languages, we calculated a Composite Rating combining transcription accuracy (WER) and translation quality (BLEU) into a single weighted score. Every tier below is production-ready. Scores reflect default performance with Krisp’s built-in domain dictionaries active.
| Tier |
Rating |
Languages |
What it means |
| Excellent |
69–71 |
French, Italian, Spanish, Norwegian, Swedish |
Top-tier quality for high-stakes customer-facing use |
| Strong |
64–67 |
Dutch, Danish, Greek, French (Canadian), Indonesian, Bulgarian, Filipino, Portuguese (PT) |
High-quality across all domains |
| Solid |
56–63 |
Russian, German, Hindi, Arabic, Vietnamese, Ukrainian, Hebrew, Romanian, Chinese, Korean |
Dependable for general business use |
| Functional |
44–53 |
Czech, Polish, Finnish, Hungarian, Turkish, Japanese |
Reliable quality; Custom Vocabulary and Dictionary recommended |
How we measured translation accuracy
Transcription was measured using Word Error Rate (WER), the industry standard for speech recognition accuracy. Top languages like Italian (2.07%) and Spanish (2.11%) achieve WER under 2.5%.
Translation was measured using BLEU, the standard for machine translation quality, scored bidirectionally (English→target and target→English):
| Language |
→English BLEU |
→Target BLEU |
| French |
62.96 |
56.67 |
| Norwegian |
65.73 |
51.66 |
| Spanish |
62.86 |
54.56 |
| Swedish |
62.54 |
53.57 |
| Italian |
60.70 |
51.06 |
We also used chrF++, a character-level metric that complements BLEU for languages with complex word forms (Turkish, Finnish, Hungarian), where BLEU alone can understate quality.
Accuracy QA, Krisp’s AI-driven semantic scoring, independently validated every conversation across intent accuracy (35%), entity accuracy (30%), conversation flow (25%), and naturalness (10%). Scores averaged 94–96 across all 30 languages, confirming real-world usability alongside the objective metrics.
Bilingual human review by professional linguists across 8 languages independently confirmed the automated findings.
Domain performance
Quality was consistent across all six business domains – finance, healthcare, insurance, retail, travel, universal – with no significant drops in specialized scenarios. Krisp ships with built-in domain dictionaries for each, active by default.
Accuracy that improves with use
The benchmarks above reflect default performance. From there, accuracy can be further sharpened:
- Custom Vocabulary improves transcription of company-specific terms, product names, and internal codes
- Custom Dictionary controls how specific terms are translated per language pair
- Agent submissions let agents flag misrecognized terms directly from their app
- Accuracy QA suggestions systematically surface terms that should be added based on post-call analysis
Four input channels, one outcome: a system that adapts to each deployment and gets more accurate over time.
61 languages and growing
Voice Translation supports 61 production languages, with 30 rigorously benchmarked and 31 additionally available. New additions include French (Canada), Spanish (US), Arabic (Egypt), Catalan, Galician, and Basque, reflecting a move toward locale-specific and regional precision.
Want the full benchmark data? Contact our team for the complete Voice Translation Quality Evaluation report, including per-language scores and per-domain breakdowns.
Book a demo → Explore what VT v3 can do for your operation.
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FAQ
How accurate is AI voice translation on live calls?
Krisp’s Voice Translation achieves 93-97% semantic accuracy across 30 benchmarked languages, measured by Accuracy QA on live production calls — not clean studio recordings. English transcription accuracy (WER) is ~2.7%, and top-language BLEU scores range from 51-66, comparable to professional human translation.
Which AI is best for voice translation?
For live speech-to-speech translation in production environments, accuracy depends on language pair, domain, and audio conditions. Krisp’s Voice Translation is benchmarked across 30 languages and 6 business domains (finance, healthcare, insurance, retail, travel) with published accuracy scores. Key differentiators include built-in noise cancellation for noisy audio environments and custom vocabulary for domain-specific terminology — features most translation APIs don’t offer.
What are WER and BLEU in translation accuracy?
WER (Word Error Rate) measures speech recognition accuracy — the percentage of words incorrectly transcribed. Lower is better; Krisp’s top languages achieve under 2.5%. BLEU (Bilingual Evaluation Understudy) measures translation quality by comparing machine output to human reference translations on a 0-100 scale. Professional human translations typically score around 60; Krisp’s top languages score 51-66.
