Multilingual transcription for global qualitative research: challenges and solutions

When running qualitative research across multiple markets, research design, moderation, and analysis all carry extra weight, since participants speak different languages and sometimes switch between them mid-sentence. This complexity carries over to transcription. Get that wrong, and it spills over to every stage that follows- coding, analysis, reporting. This post covers the real challenges of multilingual transcription in global qual research and the practical approaches that actually resolve them. 

The core challenge 

Over the past decade or so, AI transcription has improved dramatically for English. However, AI transcription’s performance in non-English languages is still uneven. Relatively common non-English languages, e. French, German, Spanish, and Portuguese do get close to English-level accuracy, provided the audio is clean. Accuracy drops significantly for lower-resource languages, regional dialects, and heavily accented speech. In fact, in some cases, accuracy can fall below 80% (which is not analysis-ready by any standard). 

Now let’s add on more complexity layers... those specific to qualitative research: 

• Code-switching: Participants moving between two languages in a single turn. This is common in multilingual markets and breaks most AI transcription models, which are trained on monolingual audio
  
  

• Non-Latin scripts: Languages with unique scripts, e.g., Arabic / Japanese / Chinese / Korean, require models specifically trained on those scripts. Generic automated speech recognition (ASR) tools often produce inconsistent output or fail altogether.
  
  

• Colloquial and idiomatic language: Qualitative research captures how people actually talk, including slang, cultural references, and regional expressions... all elements that standard training models do not cover well.
  
  

• Crosstalk in focus groups: In a multilingual group where participants default to their dominant language when excited or challenged, rapid speaker switches become very difficult for automated diarization to handle 

Transcribe in original language, or directly into English? 

This is a question often posed by teams running studies involving global research transcription. 

The ideal approach is to transcribe in the original language first, then translate. This preserves the source data in its native form, keeps the analytical layer separate from the linguistic layer, and allows the research team to go back to the original-language transcript if a translation decision is questioned. Transcribing directly into English often collapses those two steps into one and introduces translator interpretation into what should be a factual replay of the spoken word.  

AI transcription vs human transcription for non-English research 

AI transcription of non-English languages is a viable starting point for high-resource languages on clean audio. However, it is not a reliable endpoint for most global qualitative research studies. Here are choices that experienced teams typically make in various scenarios: 

All in all, human transcription by a native speaker who understands the research context (not just the language) produces consistently higher quality output for non-English qualitative data. 

Practical checklist for multilingual qual research transcription 

• Confirm which languages are in scope before fieldwork begins; do not assume your platform covers them
  
  

• For AI transcription, upload a custom vocabulary list specific to the research domain and brand names before the session
  
  

• For non-Latin script languages or low-resource languages, go straight to human transcription rather than correcting AI output
  
  

• Specify verbatim transcription for qualitative research; cleaned-up or intelligent verbatim removes hesitations like “Err...” “Umm...” (which often carry analytical weight)
  
  

• For peer-reviewed human transcription, confirm the reviewer is also a native speaker
  
  

• Ensure your transcription provider's data handling meets GDPR and HIPAA, where applicable, and does not use recordings for AI training 

myMRPlace tools handle multilingual transcription 

flowres.io supports automated transcription in 19 languages, including English, French, German, Spanish, Portuguese, Italian, Japanese, Polish, and Russian (among others), with a custom vocabulary feature for domains where standard models mishandle terminology. Human proofreading is available as an add-on for sessions where AI accuracy needs verification before analysis begins. 

For sessions in languages outside those 19, or for research requiring 99% accuracy with native-speaker review, myTranscriptionPlace covers multi-language transcription services and simultaneous interpretation (for live sessions) across 130+ languages. Every transcript is peer-reviewed by a second native linguist before delivery. They are ISO 27001 certified, GDPR and HIPAA compliant, and ESOMAR members. Recordings are never used for AI model training, which matters when participant data is collected under a defined consent framework. 

FAQs 

What is multilingual transcription? 

The process of converting audio or video recordings in multiple languages into accurate, analysis-ready text, either by AI, human transcriptionists, or a combination of both. 

How accurate is AI transcription for non-English languages? 

Accuracy varies significantly; high-resource languages like French and German achieve close to English-level accuracy on clean audio. However, low-resource languages, regional dialects, and code-switching sessions often require human review to reach similar levels of accuracy. 

Should I transcribe in the original language or directly into English? 

Transcribe in the original language first and translate separately. Collapsing the two steps introduces translator interpretation into the source record and makes it harder to defend findings if questioned. 

What is code-switching, and why does it matter for transcription? 

Participants moving between two languages in a single turn (common in multilingual markets). Most AI transcription models handle it poorly, and it typically requires human transcription to capture accurately. 

When should I use human transcription over AI? 

For non-Latin script languages, dialect-heavy or code-switching sessions, healthcare or legal research where accuracy is non-negotiable, and any session where AI accuracy drops below a level you can correct efficiently. 

How many languages does flowres.io support for transcription? 

19 languages, including English and major European languages, with custom vocabulary support. Human proofreading is available as an add-on for additional accuracy.