{"id":23297,"date":"2026-06-09T16:24:37","date_gmt":"2026-06-09T12:24:37","guid":{"rendered":"https:\/\/krisp.ai\/blog\/?p=23297"},"modified":"2026-06-09T17:07:25","modified_gmt":"2026-06-09T13:07:25","slug":"voice-translation-accuracy-benchmarks","status":"publish","type":"post","link":"https:\/\/krisp.ai\/blog\/voice-translation-accuracy-benchmarks\/","title":{"rendered":"Voice Translation accuracy: benchmarked, measured, and proven in production"},"content":{"rendered":"

How accurate is AI translation<\/strong> in real production environments? Let\u2019s deep-dive into the foundation Voice Translation v3 is built on: accuracy.<\/p>\n

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.<\/p>\n

Learn about AI voice translation for call centers<\/a>.<\/p>\n

AI translation accuracy<\/strong>: evaluation results<\/h2>\n

Krisp Voice Translation has been evaluated across 30 languages<\/strong>, 6 business domains<\/strong>, and 870 conversations<\/strong>, using three independent validation layers: automated benchmarking, AI-driven semantic scoring, and bilingual human review.<\/p>\n\n\n\n\n\n\n\n\n
Metric<\/th>\nResult<\/th>\n<\/tr>\n<\/thead>\n
English transcription accuracy (WER)<\/td>\n~2.7% (97 out of 100 words correct)<\/td>\n<\/tr>\n
Target language transcription accuracy<\/td>\n2\u201310% WER for most languages<\/td>\n<\/tr>\n
Translation quality (BLEU), top languages<\/td>\n51\u201366 (human translations typically score ~60)<\/td>\n<\/tr>\n
Semantic accuracy (Accuracy QA)<\/td>\n94\u201396 \/ 100 across all benchmarked languages<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Proven in practice: healthcare deployment<\/h2>\n

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.<\/p>\n\n\n\n\n\n\n\n\n\n
Metric<\/th>\nResult<\/th>\n<\/tr>\n<\/thead>\n
Multilingual calls completed end-to-end<\/td>\n90% (no interpreter needed)<\/td>\n<\/tr>\n
Overall translation accuracy (Accuracy QA)<\/td>\n96%<\/td>\n<\/tr>\n
Patient safety incidents<\/td>\nZero<\/td>\n<\/tr>\n
Languages in one workforce<\/td>\n8+<\/td>\n<\/tr>\n
Interpreter wait time<\/td>\n0 seconds<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Accuracy by language:<\/strong><\/p>\n\n\n\n\n\n\n\n\n
Language<\/th>\nScore<\/th>\n<\/th>\nLanguage<\/th>\nScore<\/th>\n<\/tr>\n<\/thead>\n
Spanish (US)<\/td>\n96%<\/td>\n<\/td>\nRussian<\/td>\n98%<\/td>\n<\/tr>\n
Spanish<\/td>\n97%<\/td>\n<\/td>\nVietnamese<\/td>\n96%<\/td>\n<\/tr>\n
English (US)<\/td>\n97%<\/td>\n<\/td>\nHindi<\/td>\n97%<\/td>\n<\/tr>\n
Arabic<\/td>\n97%<\/td>\n<\/td>\nKorean<\/td>\n94%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Read the full Voice Translation v3 announcement<\/a><\/em><\/p>\n

Voice Translation language quality tiers<\/h2>\n

To rank languages, we calculated a Composite Rating<\/strong> 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.<\/p>\n\n\n\n\n\n\n\n\n
Tier<\/th>\nRating<\/th>\nLanguages<\/th>\nWhat it means<\/th>\n<\/tr>\n<\/thead>\n
Excellent<\/strong><\/td>\n69\u201371<\/td>\nFrench, Italian, Spanish, Norwegian, Swedish<\/td>\nTop-tier quality for high-stakes customer-facing use<\/td>\n<\/tr>\n
Strong<\/strong><\/td>\n64\u201367<\/td>\nDutch, Danish, Greek, French (Canadian), Indonesian, Bulgarian, Filipino, Portuguese (PT)<\/td>\nHigh-quality across all domains<\/td>\n<\/tr>\n
Solid<\/strong><\/td>\n56\u201363<\/td>\nRussian, German, Hindi, Arabic, Vietnamese, Ukrainian, Hebrew, Romanian, Chinese, Korean<\/td>\nDependable for general business use<\/td>\n<\/tr>\n
Functional<\/strong><\/td>\n44\u201353<\/td>\nCzech, Polish, Finnish, Hungarian, Turkish, Japanese<\/td>\nReliable quality; Custom Vocabulary and Dictionary recommended<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

How we measured translation accuracy<\/h2>\n

Transcription<\/strong> 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%.<\/p>\n

Translation<\/strong> was measured using BLEU, the standard for machine translation quality, scored bidirectionally (English\u2192target and target\u2192English):<\/p>\n\n\n\n\n\n\n\n\n\n
Language<\/th>\n\u2192English BLEU<\/th>\n\u2192Target BLEU<\/th>\n<\/tr>\n<\/thead>\n
French<\/td>\n62.96<\/td>\n56.67<\/td>\n<\/tr>\n
Norwegian<\/td>\n65.73<\/td>\n51.66<\/td>\n<\/tr>\n
Spanish<\/td>\n62.86<\/td>\n54.56<\/td>\n<\/tr>\n
Swedish<\/td>\n62.54<\/td>\n53.57<\/td>\n<\/tr>\n
Italian<\/td>\n60.70<\/td>\n51.06<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We also used chrF++<\/strong>, a character-level metric that complements BLEU for languages with complex word forms (Turkish, Finnish, Hungarian), where BLEU alone can understate quality.<\/p>\n

Accuracy QA<\/strong>, 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\u201396 across all 30 languages, confirming real-world usability alongside the objective metrics.<\/p>\n

Bilingual human review<\/strong> by professional linguists across 8 languages independently confirmed the automated findings.<\/p>\n

Domain performance<\/h2>\n

Quality was consistent across all six business domains \u2013 finance, healthcare, insurance, retail, travel, universal \u2013 with no significant drops in specialized scenarios. Krisp ships with built-in domain dictionaries<\/strong> for each, active by default.<\/p>\n

Accuracy that improves with use<\/h2>\n

The benchmarks above reflect default performance. From there, accuracy can be further sharpened:<\/p>\n