$25/hr
The role in one line
Evaluate transcription quality and audio alignment for machine learning training data in Portuguese.
Written by Training Turk from the public listing; it may be incomplete or out of date. Read the full posting on Mercor.
What you would do
- Listen to recorded speech and determine whether transcribers captured spoken content accurately
- Verify that automated word-boundary detection placed segment start/stop points correctly
- Tag errors using a fixed set of codes and document why each judgment passed or failed
- Identify and correct misaligned segments through boundary adjustment or segment modification
Who they are looking for
- Native or fluent Portuguese (Brazil) speaker with 5+ years living in Brazil-dominant region
- Strong written English proficiency to read rulebooks and compose clear feedback
- Prior transcription, localization, linguistic annotation, or QA experience highly valued
- Comfortable with 5-hour tasks requiring sustained focus and careful listening
- Optional: phonetics background, audio editing tools familiarity, or AI annotation experience
What the interview is likely to probe
1.Distinguishing speech sounds
Model training depends on correctly identifying when speakers use similar phonemes that could be confused, since training data with acoustic errors produces flawed models.
Expect something like: “A speaker says a word sounding like 'para' but the transcriber wrote 'parra'. How would you listen to determine if this is an actual error or acceptable variation?”
2.Applying error codes consistently
Researchers use your error tags to understand what mistakes are most common, so random or inconsistent tagging undermines the dataset's value for model improvement.
Expect something like: “Three tasks have speakers inserting filler words not captured in original transcriptions. How would you tag these similarly across all three to ensure consistency?”
3.Identifying word boundaries in continuous speech
Forced alignment data trains speech recognition systems to know precisely when one word ends and the next begins, so shifted boundaries create cascading errors in models.
Expect something like: “In rapid-speech where 'o avó' and 'oavó' sound nearly identical, how would you verify the actual word boundary and correct the automated segmentation?”
4.Writing technical justifications
Your explanations help researchers understand why a segment failed and what pattern to target in retraining, turning judgment into actionable feedback.
Expect something like: “A transcription shows 'faz' but the speaker likely said 'fez'. Write your rationale in English for rejecting this segment and what the model should learn.”
Exercise you may get
Listen to a 2-minute Portuguese recording, review the provided transcription and word-level alignment, identify at least one transcription error and one boundary misalignment, tag them with error codes, and write brief justifications for each finding.
How to prepare
- Review sample transcription and alignment errors to understand the specific error codes used
- Practice distinguishing similar Brazilian Portuguese phonemes at varying speeds and noise levels
- Study the rubric document that defines when variance counts as acceptable versus error
- Complete calibration sessions comparing your judgments against expert references
Facts
- Pay
- $25/hr
- Commitment
- hourly
- Hours
- 10 per week
- Work arrangement
- remote · Remote
- Posted
- 9/18/2026