AI Transcription and Language Detection in RecRam
What this guide covers AI transcription converts video and voice responses into searchable text. Speech-to-text works in 50+ languages and is included on every paid plan. Language detection identifies the language of eac
Last reviewed: October 9, 2026
What this guide covers
AI transcription converts video and voice responses into searchable text. Speech-to-text works in 50+ languages and is included on every paid plan. Language detection identifies the language of each response, which helps when you collect responses in more than one language.
Transcription is one of five AI features in RecRam, alongside sentiment analysis, keyword extraction, executive summaries, and language detection. This guide covers transcription and language detection. For the others, see AI Sentiment, Keywords, and Executive Summaries.
How transcription fits into your review
- Decide what you want the AI output to help you decide.
- Read the transcript together with the sentiment, keywords, and summary for each response, not on its own.
- Cross-check critical responses by watching or listening to them before you make a final decision.
- Tag recurring themes and assign actions by urgency.
- Track over time whether review gets faster and decisions get better.
Language detection for multilingual responses
If your audience answers in several languages, language detection helps you sort and route responses. Use it as a first pass, and have someone who speaks the language check any response that matters for a decision.
Troubleshooting
A transcript is missing for a response
- Confirm the response contains video or voice. Transcription converts video and voice to text.
- Check that the response has finished processing. Transcripts are generated automatically for completed video responses.
- Reproduce the issue with one controlled test response and note exactly what you submitted.
- Change one thing at a time and retest with the same scenario.
Language detection seems incorrect
- Reproduce the issue with one controlled test response in a known language.
- Cross-check the result manually against the recording.
- Treat the detected language as a signal, and use your own context for the final call.
- Write down the cases where detection was wrong so your team knows what to check.
Common mistakes
- Treating AI output as final truth without checking it against context.
- Reviewing only one AI signal instead of the full response.
- Skipping periodic quality reviews with real examples.