A practical guide based on real ADAP project rules
AI transcription tasks on Appen (CrowdGen platform) can be confusing at first, especially for new contributors.
Projects like ADAP transcription and segmentation require strict rule-based decisions, not personal interpretation.
This guide explains how these tasks really work, based on actual Appen project guidelines and real edge cases.
What Are Appen / CrowdGen Transcription Tasks?
On Appen’s CrowdGen platform, transcription tasks are used to train and evaluate AI speech models.
Your role is not to create a clean script for humans, but to:
- Accurately transcribe spoken content
- Identify the main speaker
- Handle interruptions and secondary speakers correctly
- Flag segmentation issues when audio is cut incorrectly
Precision matters more than readability.
The Main Speaker Concept (Appen ADAP Projects)
Every Appen transcription task defines a Main Speaker.
- The main speaker is the primary voice to be transcribed
- Their speech is written normally, following spelling rules
- Appen provides Main Speaker Samples to help you recognize the voice
Important detail:
Main Speaker Samples may contain more than one voice.
This is intentional:
Appen uses real audio clips so you can learn to distinguish the main speaker from background or secondary speakers.
Handling Other Speakers (Non-Primary Speaker)
When someone other than the main speaker talks:
- You do not transcribe their words
- You insert the special Appen tag:
<nonprimaryspeakertalking/>
This applies to:
- Short interruptions
- Background reactions (“yeah”, “mhm”)
- Comments from another person
Never guess or rewrite what the other speaker says.
Speech Segments vs Noise Segments on Appen
Speech Segments
A speech segment contains meaningful spoken language.
- Main speaker → transcribe text
- Other speakers →
<nonprimaryspeakertalking/>
Noise Segments
Noise segments contain no meaningful speech.
Examples:
- Music
- Laughter
- Coughing
- Throat clearing
- Environmental sounds
In Appen noise segments:
- You do not transcribe words
- You use one noise tag only
- There is no main speaker
Segmentation Errors (Incorrect Segmentation)
Appen requires contributors to identify when audio is cut incorrectly.
You should flag Incorrect Segmentation when:
- A word is cut at the beginning or end
- A sentence is interrupted unnaturally
- The audio timing clearly does not match natural speech
Segmentation flags help Appen improve dataset quality.
Cut or Unclear Words
If a word is partially missing due to audio cut:
- Do not reconstruct it
- Do not guess the intended word
- Transcribe only what is audible
Appen guidelines explicitly discourage interpretation.
Fillers and Hesitations
If the main speaker uses fillers (“uh”, “um”, “ehm”):
- Transcribe them exactly as specified in the project rules
If a non-primary speaker produces fillers:
- Do not transcribe
- Use
<nonprimaryspeakertalking/>
Language and Translation Rules
On Appen transcription tasks:
- Never translate
- Always transcribe in the original spoken language
- Foreign-language flags are used only when required by the project
Common Mistakes New Appen Contributors Make
❌ Guessing unclear words
❌ Transcribing secondary speakers
❌ Mixing speech and noise rules
❌ Ignoring segmentation problems
❌ “Cleaning up” grammar
Appen evaluates rule compliance, not writing style.
Final Advice for Appen Contributors
If you are unsure, ask yourself:
“What would help Appen’s AI models learn from this audio?”
Follow the rules exactly, avoid assumptions, and stay consistent.
That’s how high-quality contributors succeed on CrowdGen.
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