Multilingual Transcription: Transcribe Audio in 90+ Languages

Most transcription guides assume you're working with English audio. But if your recordings are in Spanish, Japanese, Norwegian, Arabic, or any other language, the process and the challenges are different. Here's what you need to know about multilingual transcription.

The non-English transcription problem

Many transcription services either don't support non-English languages, support them poorly, or charge a premium for them. Some services list "multilingual support" but in practice only handle a handful of major languages well.

Modern AI transcription has changed this. The latest speech models are trained on massive multilingual datasets and can handle 90+ languages with high accuracy — often matching or exceeding English performance for well-resourced languages like Spanish, French, German, Portuguese, and Japanese.

Who needs multilingual transcription?

• Academic researchers conducting fieldwork interviews in local languages
• Journalists covering international stories or interviewing non-English speakers
• Translators who need a source-language transcript before translating
• International businesses transcribing meetings held in multiple languages
• Immigrant families preserving oral histories and stories from older relatives
• Language learners transcribing conversations or lessons for review
• Content creators reaching audiences in their native language

How language selection works

When you upload a fileto TranscribeCat, you'll see a language dropdown. You have two options:

• Auto-detect: The AI identifies the language automatically. This works well when the entire recording is in one language.
• Manual selection: Choose the language explicitly. This improves accuracy for languages that might be confused with similar-sounding ones (e.g., Norwegian vs. Swedish, Spanish vs. Portuguese).

Supported languages

TranscribeCat supports 90+ languages including all major world languages and many regional ones. Some of the most commonly used:

Mixed-language recordings

Real conversations don't always stay in one language. Bilingual speakers switch between languages naturally, and interviews might include questions in one language with answers in another.

The AI handles this better than you might expect. Speaker labels help identify who is speaking which language, and the transcript preserves each language as spoken. You won't get automatic translation — the transcript reflects what was actually said in each language.

Accuracy by language family

Based on our daily use of the OpenAI Whisper-class engine in production, accuracy varies meaningfully by language family. Cleaner audio matters more for some families than others.

Tips for transcribing tonal languages (Mandarin, Vietnamese, Thai)

Tonal languages encode lexical meaning in pitch contour. A single syllable can mean four different words depending on whether the tone rises, falls, dips, or stays level. AI accuracy on tonal languages is gated almost entirely on how cleanly the recording captures pitch — background music, low bitrates, and dynamic range compression all flatten tonal contour and produce wrong-word substitutions. Use a directional microphone, record at 44 kHz or higher, and avoid heavily compressed phone-call audio. Manual language selection (rather than auto-detect) helps because tone-language confusion at the language-ID stage cascades into worse transcription. Expect strong output for Mandarin, conversational Vietnamese, and standard Thai; expect more cleanup for Cantonese (less training data than Mandarin) and Lao.

Tips for transcribing Romance languages (Spanish, French, Italian, Portuguese)

Romance languages are some of the strongest output from modern AI transcription — training data is abundant and the phonemic systems are relatively well-segmented. The two specific traps are regional accent variation (Argentinian Spanish, Quebec French, Brazilian vs European Portuguese all produce different outputs) and code-switching. If your speaker mid-sentence drops English loanwords ("el manager", "une startup"), the AI handles it but may render the loanwords inconsistently — sometimes English-spelled, sometimes phonetically transcribed. For research interviews, do a final pass to normalize loanword spelling. Italian and standard Castilian Spanish are essentially solved; Catalan and Galician work well; Romanian works but with more proper-noun cleanup.

Tips for transcribing Arabic and Hebrew

Arabic transcription splits into Modern Standard Arabic (MSA, the lingua franca of news and formal speech) and dialectal varieties (Egyptian, Levantine, Gulf, Maghrebi). MSA accuracy is excellent; dialects are good-to-fair, with Egyptian being the strongest dialect in training data. The output is right-to-left text — check that your downstream tool preserves direction marks (RTL/LTR markers can get stripped on copy/paste). Diacritics (tashkeel) are often dropped; if you need them for academic work, you'll add them manually. Hebrew is similar — strong on standard modern Hebrew, weaker on liturgical or archaic registers, RTL output. Both languages benefit from clean studio-quality audio more than they do from manual language selection.

Tips for transcribing CJK languages (Japanese, Chinese, Korean)

CJK languages don't use spaces between words, so the AI has to do word segmentation as part of transcription. This sometimes produces output that's technically correct but reads oddly to a native speaker — particle boundaries off by one character, or compound nouns split where a fluent reader wouldn't split them. Japanese mixes hiragana, katakana, and kanji; you'll get appropriate-script output most of the time but expect occasional kanji vs hiragana inconsistency for words with both common spellings. Korean transcription is strong; output is hangul (no romanization). Mandarin output uses simplified characters by default; if you need traditional, post-process with a tool like OpenCC. All three benefit significantly from naming proper nouns ahead of time — speaker names, company names, place names — because the AI defaults to the most common reading.

Tips for transcribing Nordic and Slavic languages

Nordic languages (Norwegian, Swedish, Danish, Finnish, Icelandic) get strong output for the three big ones (no, sv, da) and good-to-fair output for Finnish and Icelandic. The classic trap is Norwegian dialect variation — Bokmål vs Nynorsk vs spoken dialects from Bergen, Stavanger, Trøndelag — but modern AI handles this surprisingly well. Slavic languages (Russian, Polish, Czech, Ukrainian, Bulgarian) are good across the board; the main complication is morphological case marking, where the same noun appears in seven different forms depending on grammatical role. Output preserves case correctly; the issue is consistency for proper nouns across forms (a name might appear as "Иван" in nominative and "Ивана" in accusative — both correct). For special characters (æ, ø, å, đ, ł, š), output uses the correct Unicode codepoints — make sure your downstream tool isn't stripping them.

General tips for better non-English transcription

• Select the language manually instead of relying on auto-detect, especially for less common languages.
• Record in quiet environments. Background noise affects accuracy more for tonal languages (Chinese, Thai, Vietnamese) where pitch carries meaning.
• Use good microphones for languages with subtle consonant distinctions (Arabic, Hindi) or vowel-heavy languages (Finnish, Japanese).
• Review proper nouns. AI transcription may struggle with names and places that are uncommon in training data, regardless of language.
• Expect great results for major languages. Spanish, French, German, Portuguese, Japanese, Chinese, and Korean are extremely well-supported. Smaller languages (e.g., Welsh, Basque, Swahili) work but may need more review.

Bottom line

If your recordings aren't in English, you don't need a specialized service or a premium plan. Modern AI transcription handles 90+ languages well, and at a flat $2/hr with no language surcharge, it's accessible to anyone — whether you're a student transcribing lectures in Spanish or a researcher with interviews in Mandarin.