An enterprise LLM developer (illustrative) · Enterprise GenAI · Text, Audio
Transcription QA and linguistic review of human and AI output
Native-speaker linguistic review for quality control — guideline compliance, error correction, and evaluation of both human and AI-generated transcription output.
By Cognegica Quality & Standards · QA & annotation-standards team
Native-linguist review · guideline-compliant
Languages: Multiple Indic languages from the registry
Illustrative scenario. This case study describes a representative methodology rather than a specific client engagement.
Challenge
An enterprise team was mixing human and AI-generated transcripts and couldn't tell which met their quality bar. They needed an independent linguistic review layer to catch errors, check guideline compliance and evaluate AI output against native-speaker judgement.
Approach
We ran the transcription-QA discipline from the portfolio:
- Native-speaker linguistic review as a dedicated quality-control layer, separate from production.
- Guideline-compliance checks against the project's transcription standard (orthography, diarization, event tags, formatting).
- Error correction with documented categories so recurring issues feed back into guidelines.
- Evaluation of human and AI outputs side by side, so the team knows where AI transcription is and isn't trustworthy.
Outcome
A documented QA pass over mixed human and AI transcription — guideline-compliant, error-corrected, and with AI output evaluated against native-speaker judgement — so the team could route work to the right producer with confidence.
Representative engagement illustrating Cognegica's transcription-QA methodology. Pass rates and error metrics are scoped per project and available under NDA.
The QA layer
Linguistic review workflow
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1
Independent linguistic review
Native-speaker linguists review output as a dedicated QC layer, separate from production.
Reviewer independence
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2
Check guideline compliance
Verify orthography, diarization, event tags and formatting against the project standard.
Compliance checklist
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3
Correct and categorise errors
Errors corrected and categorised so recurring issues feed back into guidelines.
Error categories logged
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4
Evaluate human vs AI output
Human and AI transcripts evaluated side by side against native-speaker judgement.
Comparative evaluation recorded
How this maps to what we do
The services and data behind this engagement
This outcome was delivered with the same rights-cleared, documented services and datasets you can engage today.
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Data Annotation
Two-pass QA and native-linguist review across speech and text.
Explore the service -
Cultural & Cross-Lingual Evaluation
Evaluate AI output against native-speaker judgement.
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Rare-Language Conversational Speech Corpus
QA-reviewed conversational speech in the catalog.
View data card
About this engagement
Questions buyers ask about transcription QA
- Can you evaluate AI-generated transcripts?
Yes. Native-speaker linguists evaluate AI output against the project standard and against human transcripts, so you know where AI transcription is reliable and where it isn't.
- What does the review check?
Guideline compliance across orthography, diarization, non-speech event tags and formatting, plus error correction with documented categories.
- Do review findings improve the guidelines?
Yes. Categorised errors feed back into the transcription guidelines so recurring issues are designed out over time.
Know which transcripts you can trust.
See how we structure engagements and indicative pricing, or tell us your languages, modalities and quality bar for a scoped quote.
Written by
Cognegica Quality & Standards
QA & annotation-standards team
Cognegica Quality & Standards is the internal team that defines and enforces our annotation guidelines, multi-layer QA, native-linguist review and inter-annotator agreement reporting. This is an editable team identity — a named reviewer with a public profile can be assigned to it later in the admin.