We are sharing a specialised remote consulting opportunity for Cantonese language professionals with strong transcription, annotation, and linguistic quality-review skills to contribute to an advanced AI training project focused on high-quality Cantonese and English audio data.
Selected professionals will transcribe and annotate audio recordings, review automatic transcriptions, identify linguistic nuances and speaker variations, and produce clean, time-stamped transcripts that meet defined quality standards. The work requires native-level Cantonese proficiency, excellent attention to detail, and strong sensitivity to dialect, context, and spoken-language variation. No prior experience in AI is required.
Key Responsibilities
Cantonese & English Audio Transcription
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Transcribe Cantonese and English audio recordings with a high degree of accuracy and consistency
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Capture spoken content faithfully while preserving relevant context and speaker intent
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Handle regional accents, dialectal variation, and natural speech patterns carefully
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Produce clean, structured transcripts that accurately reflect the underlying audio
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Maintain consistency across repeated transcription assignments
Annotation & Automatic Transcript Review
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Review and refine automatically generated transcriptions
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Correct errors involving wording, punctuation, segmentation, or interpretation
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Annotate audio and transcript content according to project-specific linguistic guidelines
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Identify language nuances, contextual cues, and ambiguous passages requiring further review
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Apply transcription and annotation standards consistently across assignments
Timestamping & Quality Assurance
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Deliver time-stamped transcripts according to defined formatting requirements
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Proofread and validate completed work for accuracy, completeness, and consistency
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Identify and correct transcription or annotation errors before submission
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Ensure deliverables meet established quality benchmarks
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Maintain strong attention to detail throughout the transcription and review process
Linguistic Collaboration & Project Delivery
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Collaborate with project leads to resolve unclear or ambiguous audio content
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Communicate linguistic edge cases and transcription challenges clearly
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Contribute Cantonese-language insights that improve the quality of AI training data
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Incorporate reviewer feedback and evolving project guidelines into future work
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Submit deliverables according to established project timelines and milestones
Ideal Profile
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Native proficiency in Cantonese
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Strong understanding of regional Cantonese dialectal variation
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Demonstrated professional experience in audio transcription
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Experience working with both Cantonese and English audio content is strongly preferred
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Exceptional attention to detail, accuracy, and linguistic sensitivity
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Proficiency with transcription tools and audio playback software
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Ability to type effectively in both traditional and simplified Chinese
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Strong written and verbal communication skills
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Experience reviewing or annotating linguistic data for technology, media, research, or related projects is advantageous
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Comfortable working independently and managing assignments in a remote environment
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Reliable ability to meet project quality standards and deadlines
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No prior experience in AI training is required
Engagement Details
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Independent contractor engagement
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Fully remote
- Compensation: $20–$35/hour
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Work will involve Cantonese and English audio transcription, annotation, automatic-transcript review, timestamping, and linguistic quality assurance
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Native-level Cantonese proficiency and strong transcription accuracy are central to this engagement
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Experience with traditional and simplified Chinese input is highly relevant
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Project workload, timelines, formatting standards, and annotation requirements may vary depending on project needs
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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