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•12 min read•BrassTranscripts Team

Non-English Transcription: 99 Language AI Guide

Ai TranscriptionTranscription AccuracyAudio Transcription

BrassTranscripts supports transcription in 99+ languages—but not all languages perform equally. This guide covers which languages achieve the highest accuracy, what to expect from less-resourced languages, and how to optimize recordings for multilingual and non-English transcription.

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Language Tier System: Accuracy by Language

OpenAI's Whisper model was trained on 680,000 hours of multilingual audio data. However, training data is heavily skewed toward certain languages. This creates predictable accuracy tiers:

Tier Languages Training Data Expected Accuracy
Tier 1 English, Spanish, German, French, Italian, Portuguese, Japanese Abundant Professional-grade
Tier 2 Dutch, Polish, Russian, Korean, Mandarin, Arabic, Hindi, Turkish Good Good (slight reduction)
Tier 3 Most other 80+ languages Limited Variable (may need review)

Key insight: Accuracy correlates directly with training data volume. Languages with more internet content, media, and transcribed audio produce better results.

Tier 1 Languages: Highest Accuracy

These languages have the most training data and produce the most reliable transcription:

English

  • Dialects covered: American, British, Australian, Indian, South African, Irish, Scottish
  • Performance: Professional-grade accuracy on clear audio
  • Strengths: Technical vocabulary, medical terminology, legal language
  • Watch for: Heavy accents combined with fast speech or mumbling

Spanish

  • Dialects covered: Mexican, Colombian, Argentine, Castilian, Caribbean
  • Performance: Very strong across all major dialects
  • Strengths: Handles accent variations well
  • Watch for: Regional slang may produce unexpected spellings

German

  • Dialects covered: Standard German, Austrian, Swiss German
  • Performance: Excellent for standard and Austrian German
  • Strengths: Compound words handled correctly
  • Watch for: Swiss German may show reduced accuracy due to dialect variation

French

  • Dialects covered: Metropolitan French, Canadian French, Belgian French, African French
  • Performance: Very strong for European French
  • Strengths: Liaison and elision handled well
  • Watch for: Canadian French shows slightly reduced accuracy; African French varies by region

Italian

  • Performance: Excellent for standard Italian
  • Strengths: Clear consonant sounds produce reliable transcription
  • Watch for: Strong regional dialects (Sicilian, Neapolitan) may be transcribed as standard Italian

Portuguese

  • Dialects covered: Brazilian Portuguese, European Portuguese
  • Performance: Very strong for Brazilian Portuguese; good for European
  • Strengths: Brazilian Portuguese has extensive training data
  • Watch for: European Portuguese shows slightly more variability

Japanese

  • Performance: Strong for standard Japanese
  • Strengths: Handles kanji, hiragana, and katakana output
  • Watch for: Specialized business terminology; regional dialects

Tier 2 Languages: Good Accuracy

These languages have solid training data but may show slight accuracy reduction compared to Tier 1:

Mandarin Chinese

  • Performance: Good accuracy for Standard Mandarin (Putonghua)
  • Output: Chinese characters (simplified or traditional based on context)
  • Strengths: Handles tones contextually
  • Watch for: Cantonese and other Chinese languages may be transcribed as Mandarin; technical terminology

Arabic

  • Dialects covered: Modern Standard Arabic, Gulf Arabic, Egyptian Arabic
  • Performance: Best for Modern Standard Arabic; variable for dialects
  • Output: Arabic script (right-to-left)
  • Watch for: Dialectal Arabic may be normalized to MSA; religious and technical terms

Hindi

  • Performance: Good for standard Hindi
  • Output: Devanagari script
  • Watch for: Urdu overlap; regional variations; English code-switching common

Russian

  • Performance: Strong for standard Russian
  • Output: Cyrillic script
  • Watch for: Technical terminology; names and places

Korean

  • Performance: Good for standard Korean
  • Output: Hangul script
  • Watch for: Formal vs informal speech levels; technical English loanwords

Dutch

  • Performance: Good for standard Dutch
  • Watch for: Belgian Dutch (Flemish) shows slightly reduced accuracy

Polish, Turkish, Vietnamese, Thai

  • Performance: Generally good
  • Watch for: Less common vocabulary; regional variations

Tier 3 Languages: Variable Accuracy

These languages have limited training data. Results may require more human review:

European languages with limited data:

  • Romanian, Bulgarian, Croatian, Serbian, Slovak, Czech, Hungarian, Greek
  • Scandinavian: Norwegian, Swedish, Danish, Finnish, Icelandic
  • Baltic: Lithuanian, Latvian, Estonian

Asian languages with limited data:

  • Indonesian, Malay, Tagalog/Filipino
  • Burmese, Khmer, Lao
  • Regional Indian languages: Tamil, Telugu, Bengali, Gujarati, Marathi

African languages:

  • Swahili, Yoruba, Zulu, Amharic, Hausa
  • Generally show the most variability; may require significant review

Middle Eastern languages:

  • Persian (Farsi), Hebrew, Urdu
  • Hebrew shows better accuracy due to more training data

Setting Expectations for Tier 3 Languages

For languages in this tier:

  • Expect some words or phrases to be mistranscribed
  • Plan for human review of important content
  • Consider the transcript a "first draft" that accelerates manual work
  • Test with sample audio before committing to large projects

Even imperfect transcription significantly reduces the time required versus starting from scratch. A transcript that's 70% accurate still eliminates most manual work.

Accented English: What to Expect

AI transcription handles most English accents effectively because:

  1. English has the most training data
  2. Accent variation is well-represented in training
  3. Context helps resolve unclear pronunciations

Accents That Transcribe Well

Indian English: Well-represented in training data due to large English-speaking population. Strong performance.

British English: All major variants (RP, Scottish, Welsh, regional) perform well.

Australian English: Strong performance including slang terms.

South African English: Good performance with occasional Afrikaans influence.

American Regional: Southern, Boston, New York, Midwest—all perform well.

Factors That Reduce Accuracy (More Than Accent)

Speech rate: Fast speech combined with any accent reduces accuracy more than accent alone.

Audio quality: Background noise or poor microphones affect accented speech more than clear audio.

Technical jargon: Domain-specific vocabulary may be misheard regardless of accent.

Mumbling or trailing off: Incomplete articulation is the primary accuracy issue, not accent.

Optimizing Recordings for Accented English

  1. Use good microphones: Clear audio compensates for accent variation
  2. Speak at moderate pace: Slightly slower than natural helps significantly
  3. Enunciate technical terms: Spell out acronyms on first use
  4. Reduce background noise: Quiet environments help the model focus on speech

Multilingual Audio: Code-Switching and Mixed Content

How WhisperX Handles Language Mixing

WhisperX performs automatic language detection and can handle:

Sequential multilingualism: Different speakers using different languages in the same recording. The system detects language changes between speakers.

Code-switching: Switching languages mid-conversation (common in multilingual communities). Results vary based on how clearly languages are separated.

Loanwords: English technical terms in non-English conversations are usually captured correctly.

Best Practices for Multilingual Recordings

Clearly separated languages work best:

  • Meeting in Spanish with English technical terms: Works well
  • Sentence that switches language mid-phrase: May produce errors

Use JSON output for language analysis: The JSON format includes detected language per segment, helping you identify which parts are in which language.

Consider separate processing: For critical multilingual content, you might process the file twice—once for each language—and combine results.

International Research Interviews

Academic researchers frequently transcribe interviews conducted in participants' native languages:

Workflow recommendation:

  1. Transcribe in original language using BrassTranscripts
  2. Review transcription for accuracy (native speaker preferred)
  3. Translate if needed using professional services or AI translation

This preserves the original language data for analysis while enabling translation for reporting.

Language-Specific Tips

Mandarin Chinese

Tonal language considerations: WhisperX handles tones contextually—it determines meaning from surrounding words rather than explicitly marking tones.

Script output: Output is in Chinese characters. If you need pinyin (Romanized), use a secondary tool to convert.

Homophones: Context usually resolves homophones, but technical contexts may produce errors.

Best practices:

  • Clear pronunciation helps
  • Standard Mandarin (Putonghua) performs best
  • Regional Mandarin accents may show reduced accuracy

Arabic and Its Dialects

Modern Standard Arabic (MSA): Best accuracy. Use for formal content.

Dialectal Arabic: Egyptian, Gulf, and Levantine dialects may be partially normalized to MSA.

Script direction: Output is right-to-left Arabic script. Most text editors handle this correctly.

Best practices:

  • Formal, clearly articulated speech produces best results
  • Colloquial dialect conversations may need more review

European Languages

Germanic languages (German, Dutch, Swedish, Norwegian, Danish):

  • Compound words are usually handled correctly
  • Swedish and Norwegian show good accuracy
  • Danish may require more review due to pronunciation complexity

Romance languages (Spanish, Portuguese, French, Italian, Romanian):

  • All major Romance languages perform well
  • Romanian shows more variability than others

Slavic languages (Russian, Polish, Czech, Ukrainian):

  • Russian and Polish have good training data
  • Other Slavic languages show more variability

Use Cases for Non-English Transcription

International Business Meetings

Scenario: Multinational team meeting with speakers in multiple languages.

Approach:

  • Record the meeting
  • Process through BrassTranscripts
  • System detects language per speaker segment
  • Translation handled separately if needed

Output value: Searchable record of who said what, action items in original language, foundation for translation.

Academic Research

Scenario: Research interviews conducted in participants' native languages (Hindi, Arabic, Portuguese, etc.).

Approach:

  • Transcribe in original language to preserve authentic participant voice
  • Review with native speaker for accuracy
  • Translate key quotes for publications

Why this matters: IRB requirements often specify preserving original language; cultural context embedded in word choice.

Multilingual Content Creation

Scenario: Podcast with guests speaking different languages; YouTube content targeting international audiences.

Approach:

  • Transcribe original content in source language
  • Generate subtitles (SRT/VTT) in original language
  • Translate subtitles for additional language tracks

Example: A Spanish podcast transcribed in Spanish, then subtitles translated to English and Portuguese for broader reach.

Scenario: Depositions, witness statements, or compliance interviews in non-English languages.

Approach:

  • Transcribe in original language as foundational document
  • Certified human translation for official records
  • AI transcription accelerates the process; human review ensures compliance

Caution: Legal proceedings may require certified human transcription. AI transcription serves as working draft, not official record.

Global Market Research

Scenario: Focus groups conducted in local languages across multiple countries.

Approach:

  • Transcribe each session in native language
  • Analyze themes within each language first
  • Translate key insights for cross-market comparison

Advantage: Preserves nuance and cultural context that may be lost in simultaneous translation.

Common Questions

Why does accuracy vary so much between languages?

AI models learn from training data. Languages with more written and transcribed content on the internet—English, Spanish, German, French—have vastly more training data. A language with 100,000 hours of training audio will outperform one with 1,000 hours.

Should I use language-specific transcription services for non-English content?

For Tier 1 languages (English, Spanish, French, German, Italian, Portuguese, Japanese), WhisperX performs comparably to or better than most alternatives. For Tier 3 languages, specialized services with language-specific models may offer better accuracy—but often at higher cost.

How do I verify accuracy for a language I don't speak?

Options:

  1. Native speaker review: Most reliable
  2. Back-translation test: Translate to English, then back to original language—major errors become obvious
  3. Spot-check with translation: Select random segments and verify meaning
  4. Compare to audio: Even without speaking the language, you can match timing and detect obvious errors

Can I improve accuracy for specific terminology?

WhisperX doesn't support custom vocabulary training. However:

  • Providing context in how you use the transcript (AI prompts) can help interpret terminology
  • Consistent terminology in the audio itself helps
  • Post-processing with find-and-replace for known terms works well

What about automatic translation after transcription?

BrassTranscripts provides transcription in the original language. For translation:

  1. Use the TXT output with Google Translate, DeepL, or ChatGPT
  2. For professional needs, use human translation services
  3. AI translation works well for internal use; human review recommended for publication

Frequently Asked Questions

How many languages does BrassTranscripts support?

BrassTranscripts supports 99+ languages. However, accuracy varies significantly by language. English, Spanish, German, French, Italian, Portuguese, and Japanese achieve the highest accuracy. Less-resourced languages may have 10-30% lower accuracy depending on training data availability.

Can I transcribe audio in multiple languages at once?

Yes. WhisperX automatically detects language switches within audio. If someone switches between English and Spanish mid-sentence (code-switching), the system attempts to capture both. Accuracy is higher when languages are clearly separated rather than mixed within sentences.

Is accented English handled well by AI transcription?

Modern AI transcription handles most English accents effectively—Indian, British, Australian, South African, and regional American accents are well-represented in training data. Heavy accents combined with fast speech or technical jargon may reduce accuracy. Clear pronunciation helps more than accent reduction.

What's the best format for multilingual transcription output?

For multilingual audio, JSON format provides the most detail—including detected language per segment. TXT format shows the transcription but not language markers. If you need to identify which parts are in which language, use JSON output.

How do I transcribe languages with non-Latin scripts?

WhisperX outputs text in the original script—Mandarin in Chinese characters, Arabic in Arabic script, Russian in Cyrillic. If you need Romanized output (pinyin for Mandarin, transliteration for others), you'll need a secondary processing step or use a translation service.

Transcribe in 99+ Languages

Whether you're transcribing international research interviews, multilingual business meetings, or content in your native language, BrassTranscripts processes audio in 99+ languages with automatic language detection.

Upload your non-English audio → and get transcripts with speaker identification in minutes. Tier 1 languages (English, Spanish, German, French, Italian, Portuguese, Japanese) achieve professional-grade accuracy. Other languages provide strong starting points that accelerate manual review.

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