Independent benchmark · Measured 2026-09-17
Speech Recognition Accuracy: 95 Languages
Pikka Speech transcribes 95 languages with a median character error rate of 3.1%. OpenAI gpt-transcribe scores 5.1%, ElevenLabs Scribe v2 3.8%, Google Chirp 3 12.2% and Gemini 3.5 Transcribe 6.6% on the same audio — lower is better.
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Full table
Character error rate by language
Character error rate (CER) per language, lower is better. Engines marked n/a returned no usable transcript for that language. Bold marks the best result in each row. † measured on OpenSLR (see method).
| Language | Pikka Speech | OpenAI gpt-transcribe | ElevenLabs Scribe v2 | Google Chirp 3 | Gemini 3.5 Transcribe |
|---|---|---|---|---|---|
| Afrikaans | 2.8% | 3.0% | 1.5% | 10.7% | 5.2% |
| Amharic | 3.9% | 14.9% | 10.7% | 12.3% | 5.9% |
| Arabic | 4.1% | 5.4% | 5.2% | 11.5% | 4.9% |
| Armenian | 12.3% | 14.2% | 12.5% | 21.1% | 20.4% |
| Assamese | 8.5% | 12.3% | 8.2% | 42.7% | 14.6% |
| Asturian | 7.6% | 9.4% | 4.2% | 10.8% | 16.0% |
| Azerbaijani | 0.9% | 2.5% | 0.9% | 28.0% | 2.5% |
| Basque† | 3.6% | 3.1% | 4.4% | 18.7% | 14.6% |
| Belarusian | 2.2% | 2.8% | 2.2% | n/a | 3.2% |
| Bengali | 0.9% | 3.2% | 3.3% | 10.2% | 1.9% |
| Bulgarian | 0.4% | 0.4% | 1.0% | 8.2% | 1.6% |
| Burmese | 9.9% | 41.6% | 8.7% | 7.9% | 13.5% |
| Cantonese | 9.7% | 4.8% | 22.7% | 12.7% | 14.8% |
| Catalan | 2.2% | 3.7% | 2.7% | 3.6% | 2.4% |
| Cebuano | 9.8% | 8.3% | 4.5% | n/a | 9.1% |
| Chinese | 4.9% | 6.8% | 7.6% | 12.8% | 7.9% |
| Croatian | 0.4% | 27.8% | 2.4% | 2.7% | 2.4% |
| Czech | 6.0% | 4.2% | 3.1% | 9.1% | 2.2% |
| Danish | 1.8% | 3.3% | 1.2% | 2.2% | 6.6% |
| Dutch | 1.8% | 0.4% | 0.6% | 14.2% | 2.4% |
| English | 3.1% | 1.5% | 2.2% | 9.7% | 1.2% |
| Estonian | 0.5% | 1.9% | 1.7% | 9.2% | 7.3% |
| Finnish | 0.3% | 0.6% | 0.2% | 8.8% | 1.0% |
| French | 3.9% | 2.5% | 0.7% | 16.8% | 3.4% |
| Galician | 1.1% | 1.5% | 1.6% | 6.1% | 3.9% |
| Georgian | 4.1% | 6.8% | 2.6% | 4.5% | 9.1% |
| German | 5.1% | 0.4% | 0.4% | 5.7% | 1.1% |
| Greek | 2.4% | 2.1% | 1.7% | 9.6% | 7.1% |
| Gujarati | 3.4% | 8.3% | 6.9% | 15.6% | 6.5% |
| Hausa | 5.9% | 17.4% | 6.6% | 7.4% | 9.4% |
| Hebrew | 6.7% | 6.1% | n/a | 27.0% | 14.2% |
| Hindi | 1.7% | 3.6% | 4.4% | 17.3% | 1.7% |
| Hungarian | 4.0% | 2.3% | 3.2% | 12.1% | 11.3% |
| Icelandic | 10.9% | 12.2% | 9.1% | 12.1% | 19.6% |
| Indonesian | 1.1% | 2.6% | 1.1% | 14.5% | 0.3% |
| Irish | 22.8% | 32.4% | 18.6% | n/a | 60.9% |
| Italian | 0.3% | 0.6% | 0.7% | 0.8% | 0.5% |
| Japanese | 1.8% | 3.6% | 1.1% | 0.9% | 2.7% |
| Javanese | 3.1% | 11.4% | 2.4% | 6.0% | 10.0% |
| Kannada | 1.6% | 4.6% | 7.3% | 11.8% | 11.2% |
| Kazakh | 0.9% | 4.5% | 2.9% | 15.1% | 2.4% |
| Khmer | 9.6% | 12.3% | 7.1% | 14.3% | 11.4% |
| Korean | 2.2% | 2.9% | 1.3% | 8.3% | 3.8% |
| Kurdish | n/a | 10.9% | 7.5% | n/a | 16.1% |
| Kyrgyz | 5.6% | 5.7% | 6.5% | 31.0% | 7.9% |
| Lao | 17.6% | 62.0% | 7.4% | 10.1% | 34.1% |
| Latvian | 2.4% | 4.0% | 2.7% | 13.9% | 6.3% |
| Lithuanian | 2.1% | 6.5% | 5.3% | 17.9% | 9.7% |
| Luxembourgish | 35.5% | 16.9% | 6.1% | 36.1% | 26.3% |
| Macedonian | 1.0% | 1.3% | 0.3% | 9.3% | 2.3% |
| Malay | 2.2% | 1.3% | 2.2% | 22.2% | 2.3% |
| Malayalam | 1.2% | 4.3% | 7.4% | 11.8% | 3.0% |
| Maltese | 12.0% | 6.7% | 2.3% | 12.9% | 16.0% |
| Maori | n/a | 16.3% | 6.5% | 21.2% | 18.0% |
| Marathi | 3.7% | 7.0% | 6.5% | 26.3% | 4.8% |
| Mongolian | 5.2% | 12.5% | 7.3% | 5.9% | 7.0% |
| Nepali | 0.0% | 7.0% | 7.0% | 45.3% | 4.7% |
| Northern Sotho | 13.2% | 34.7% | 6.4% | 29.2% | 23.6% |
| Norwegian | 0.7% | 1.0% | 3.4% | 4.2% | 3.8% |
| Odia | 7.8% | 10.6% | 6.8% | 17.0% | 8.2% |
| Oromo | n/a | 63.1% | 31.4% | n/a | 39.0% |
| Pashto | n/a | 24.1% | 22.9% | n/a | 46.1% |
| Persian | 1.9% | 2.3% | 4.4% | 3.2% | 2.8% |
| Polish | 1.6% | 1.0% | 1.9% | 12.1% | 3.3% |
| Portuguese | 1.0% | 2.8% | 3.4% | 5.4% | 3.7% |
| Punjabi | 4.6% | 23.9% | n/a | 22.1% | 4.5% |
| Romanian | 2.9% | 0.8% | 1.1% | 60.1% | 2.5% |
| Russian | 0.2% | 0.2% | 1.2% | 3.2% | 0.4% |
| Serbian | 15.1% | 88.1% | 30.0% | 81.3% | 40.9% |
| Sesotho† | 11.0% | 19.7% | 1.5% | n/a | 28.7% |
| Setswana† | 6.8% | n/a | 3.8% | n/a | 28.7% |
| Sindhi | 4.2% | 98.5% | 5.3% | n/a | 8.9% |
| Sinhala† | 15.3% | 48.8% | 2.0% | n/a | 43.5% |
| Slovak | 1.2% | 1.8% | 1.2% | 5.0% | 3.5% |
| Slovenian | 2.0% | 3.5% | 1.7% | 23.5% | 8.0% |
| Somali | n/a | 24.9% | 12.4% | n/a | 17.6% |
| Spanish | 1.2% | 0.4% | 0.5% | 2.1% | 0.8% |
| Swahili | 1.9% | 5.3% | 1.1% | 7.8% | 3.0% |
| Swedish | 4.5% | 3.8% | 2.4% | 4.0% | 6.0% |
| Tagalog | 2.3% | 4.9% | 3.1% | 8.0% | 5.3% |
| Tajik | 2.9% | 58.3% | 2.4% | n/a | 10.0% |
| Tamil | 16.8% | 18.6% | 18.3% | 28.8% | 16.5% |
| Telugu | 1.4% | 3.4% | 6.3% | 23.0% | 1.4% |
| Thai | 4.0% | 4.9% | 4.6% | 29.5% | 5.0% |
| Turkish | 0.7% | 1.0% | n/a | 44.9% | 0.9% |
| Ukrainian | 1.2% | 3.3% | 2.6% | 14.7% | 1.1% |
| Urdu | 4.7% | 90.5% | 14.7% | 29.2% | 11.4% |
| Uzbek | 7.7% | 9.2% | 7.0% | 14.1% | 12.7% |
| Vietnamese | 1.1% | 3.6% | 1.6% | 12.2% | 4.2% |
| Welsh | 5.7% | 13.2% | 4.8% | 25.8% | 21.2% |
| Wolof | n/a | 19.6% | 13.2% | 25.7% | 29.5% |
| Xhosa | 49.0% | 33.7% | 6.5% | 44.8% | 12.1% |
| Yoruba | n/a | 34.6% | 18.7% | 27.1% | 25.6% |
| Zulu | n/a | 22.9% | 7.2% | 10.3% | 14.2% |
| pt-BR | 1.0% | 2.8% | n/a | n/a | 3.7% |
Method
How we measure
A short, reproducible method — no vendor-run benchmarks, no cherry-picked clips.
Every engine transcribes the same FLEURS clips — the open multilingual speech corpus from Google used across speech-recognition research — through its own official API.
- 95 languages, 10 utterances each, native speakers reading real sentences.
- Scored as character error rate after normalising case and punctuation, so differences reflect recognition, not formatting.
- Word error rate (WER) is reported in the downloadable data for languages that use word spacing; character-based scripts (Chinese, Japanese, Thai, Khmer, Burmese, Lao) are scored on characters only.
- Every row is published, including the languages where another model leads.
- Sinhala, Basque, Sesotho and Setswana are measured on OpenSLR corpora — SLR52, SLR76 and SLR32, all CC BY-SA 4.0 — because FLEURS has no audio for them. Every other language here is FLEURS.
- Not yet included: Akan, Bosnian, Faroese, Frisian, Quechua, Romansh, Kinyarwanda, Albanian and Turkmen — no openly licensed benchmark corpus covers them yet.
Read the corpus paper for the recording methodology: FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech.
FAQ
Questions about this benchmark
Keep exploring
Which languages Pikka Speech covers: language coverage. How interpretation and captions work together: platform capabilities and hybrid simultaneous interpretation. Or start a free test room.
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