The standard advice for PhD students is to keep a research journal, use Zotero for citations, and take notes in whatever system you'll actually maintain. Nobody mentions what to do with interview recordings in three languages. I work in cognitive science with sources from Germany, Japan, and Brazil. Getting usable text from non-English audio was the bottleneck that slowed everything else down.
Audio translation — converting a spoken recording in a foreign language to English text — now works reliably for most research and professional purposes. The process is two steps: transcribe the audio to text in its original language, then translate that text to English. Each step uses specialized AI optimized for the task.
To translate audio to English: upload the audio file or URL to sipsip.ai and select the source language. The tool transcribes the audio to text in the original language. Paste that transcript into DeepL or Google Translate for the English translation. Total time for a 30-minute recording: approximately 5–8 minutes.
Why Two Steps (Transcribe Then Translate) Works Better
Several tools claim to do direct audio-to-translated-speech in a single step. In practice, the two-step approach produces better output for almost all recorded audio use cases. Here's why.
Step 1 (audio → text) uses speech recognition models trained specifically to handle audio — noise, accents, speaker overlap, and spoken language patterns that differ from written text. Deepgram's multilingual models, for instance, are trained on hundreds of hours of non-native-accented speech per language.
Step 2 (text → translated text) uses translation models trained on large parallel corpora of written text in multiple languages. These models are specifically trained for translation quality, including handling idiomatic expressions and technical vocabulary.
Combining them in a single pipeline typically means compromising both. Systems that claim to translate audio directly in one step usually transcribe first internally anyway — they just hide the intermediate transcript.
Step-by-Step: Translating Audio Files to English
What you'll need:
- The audio file (MP3, WAV, M4A, MP4) or URL (YouTube, podcast feed)
- sipsip.ai account (free tier covers up to 20 files)
- DeepL or Google Translate (both free for text translation)
Step 1: Transcribe the audio in the original language
Open sipsip.ai's Transcriber and upload your audio file or paste the URL. Under language settings, select the original language of the recording. If you're not certain of the language, the auto-detect feature identifies it from the first few seconds of audio.
Click Transcribe. For a 30-minute recording, this takes approximately 3 minutes. sipsip.ai uses Deepgram nova-3 for high-resource languages (German, Japanese, Portuguese, Spanish, French, Chinese, Arabic, and 40+ more) and Whisper-based models for lower-resource languages.
Step 2: Review the transcript
Read through the transcript before translating. Identify any proper nouns — names of people, places, organizations — that might be mistranscribed. Proper nouns are the main category where speech recognition errors occur. A 2-minute scan for proper nouns before translation prevents translation errors from propagating from a mistranscribed source.
Step 3: Translate the transcript
Paste the transcript text into DeepL (preferred for European languages) or Google Translate (broader language coverage). For research purposes, DeepL's translation quality is generally stronger for German, French, Spanish, and Portuguese. Google Translate has broader coverage for less common languages.
For research that will be cited or published, consider using a language model (ChatGPT, Claude) for the translation step instead of or in addition to machine translation — LLMs handle technical vocabulary, academic register, and contextual nuance better than standard MT systems.
Step 4: Review and clean the translation
Machine translation of correctly transcribed text from non-English research audio typically requires 10–20% correction time for technical content. The main issues: academic jargon that doesn't map cleanly across languages, idiomatic expressions, and culturally-specific references. For general content (interviews, presentations, news), correction time is lower — 5–10%.
According to TAUS Industry Report 2025, neural machine translation quality has improved to the point where MT post-editing takes 30–40% less time than it did in 2020 for high-resource language pairs. For research use where the translation will be reviewed by the researcher (not used verbatim in publication), machine translation from a clean transcript is now sufficient for most initial analysis purposes.
Language Support: What Works Well
Excellent (95%+ transcription accuracy): English, German, French, Spanish, Portuguese, Italian, Dutch, Japanese, Korean, Chinese (Mandarin), Arabic
Good (88–94%): Russian, Turkish, Polish, Swedish, Norwegian, Danish, Finnish, Hindi, Indonesian
Variable (75–87%): Regional dialects within major languages, code-switching between languages in a single recording, highly accented speech
For interviews conducted in a language I don't speak, I transcribe in the original language and translate with DeepL. For mixed-language recordings (common in research interviews where participants switch between their native language and English), I set the language to auto-detect and review the transcript for language-switching sections.
Tools Overview for Audio Translation
| Tool | Role | Cost |
|---|---|---|
| sipsip.ai | Audio transcription (Step 1) | Free tier, 20 credits |
| DeepL | Text translation (Step 2) | Free up to 500K chars |
| Google Translate | Text translation, wider language coverage | Free |
| ChatGPT / Claude | Technical translation, context-aware | Free tiers available |
The how to translate a document guide covers the parallel workflow for PDF and written document translation, which follows similar principles.
Amelia Scott is a PhD candidate in cognitive science. She works with multilingual research audio from interview studies conducted in Germany, Japan, and Brazil, and uses sipsip.ai to transcribe recordings before translation.
Frequently asked questions
I'm a PhD candidate in cognitive science. I work with multilingual research audio from interview studies conducted in Germany, Japan, and Brazil.
