Case study
Accent is a separate pool.
A partner needed 500 hours transcribed across French Canadian, Russian, and Persian — audio carrying technical terminology and distinct regional accents.
500 hours - French Canadian, Russian, Persian - project-scoped quality review — spot-check peer review
110,000+
verified language specialists
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served since 2015
Measured outcomes
Regional-accent transcription
Project overview
What landed, and what made it hard.
A partner needed 500 hours transcribed across French Canadian, Russian, and Persian — audio carrying technical terminology and distinct regional accents.
Delivery snapshot
Regional-accent transcription
- Client
- confidential data services partner
- Service
- Transcription with regional accent expertise
- Languages
- French Canadian, Russian, Persian
- Volume
- 500 hours
- Accuracy
- project-scoped quality review on spot-check peer review
Why this mattered
Outcome before process.
French Canadian is the case in miniature. It is not European French with a different accent: the vocabulary, idiom, and phonetic patterns differ enough that the qualified transcriber pool is genuinely separate.
A generic transcription service sourced from a European French pool will produce output that reads plausibly and misses the things that make the audio French Canadian.
MoniSa handled the work under ISO 9001:2015 for process control and ISO 27001:2022 for information handling. ISO 17100:2015 is scoped to translation, so it is not claimed for this work.
Fifteen transcribers were deployed across the three languages, with project-scoped quality review measured by spot-check peer review.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Regional accent is routinely treated as a preference in briefs when it is actually a sourcing constraint. Writing "French" where the audio is French Canadian silently authorizes the wrong transcriber pool.
The challenge
The problem to solve
The failure is subtle and expensive. A European French transcriber will produce a fluent transcript that normalizes Québécois vocabulary and idiom toward metropolitan forms, and the errors are invisible to a reviewer who shares the same background.
Technical terminology adds a second requirement on top of accent. A transcriber can hold the regional variety perfectly and still mis-transcribe domain terms they have never encountered.
Russian and Persian each carry their own sourcing depth question, and Persian in particular has a smaller professional transcription pool than its speaker population suggests.
Fifteen transcribers across three languages raises the consistency problem that every distributed transcription project faces: the same audio feature resolved differently by different people produces a dataset that is internally inconsistent.
The accuracy methodology matters as much as the number. Project-scoped quality review from spot-check peer review is a specific claim; project-scoped quality review from an unspecified process is not a claim at all.
Operating response
What MoniSa changed
Transcribers were sourced against the regional variety rather than the language, so French Canadian audio was handled by French Canadian transcribers rather than a general French pool.
- Source by variety, not language French Canadian audio went to French Canadian transcribers. The pools are genuinely separate and substituting one degrades the output invisibly.
- Depth per language 15 transcribers across three languages gave each one real depth instead of a single transcriber whose absence would stall a stream.
- Feedback during production Peer review and spot checks ran while work was in flight, so a convention decision propagated instead of becoming one person's habit.
- Method stated with the number project-scoped quality review is reported as spot-check peer review. An accuracy figure without its methodology cannot be checked.
Results
Measured outcomes from this engagement.
500 hours were transcribed across the three languages with project-scoped quality review, measured by spot-check peer review.
| Volume transcribed | 500 hours |
|---|---|
| Languages | French Canadian, Russian, Persian |
| Accuracy | project-scoped quality review — spot-check peer review |
| Team | 15 transcribers |
| Content | Technical terminology, regional accents |
Selection logic
What protected the result.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
Why the fit was real
Why the fit was real
The work needed accent-specific sourcing across three languages plus domain terminology support, held consistent across a fifteen-person team.
What decided the result
What decided the result
Treating French Canadian as a separate pool rather than an accent of French is what preserved the properties the client was paying to capture.
What buyers can reuse
What buyers can reuse
- Name the regional variety in the brief, not just the language. "French" authorizes a pool that will normalize French Canadian audio without anyone noticing.
- Accent is a sourcing constraint, not a preference. Ask the vendor to confirm the transcriber pool matches the variety.
- Always ask how an accuracy figure was measured. Spot-check peer review, full-pass review, and self-assessment are three different claims wearing the same number.
- Technical terminology is a separate requirement from accent. A transcriber can hold the variety and still miss the domain.
- Team depth per language prevents stalled streams. A single transcriber per language is a scheduling risk disguised as efficiency.
- Run consistency feedback during production. Inconsistency found at delivery is found too late to fix cheaply.
- A useful transcription brief names the variety, the domain vocabulary source, the accuracy methodology, and the per-language team depth.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- French Canadian
- Russian
- Persian
- Regional accent sourcing
More proof
Related proof
Compare this case with Long-form transcription across 4 locales and Audio transcription at standing scale to judge whether the operating pattern fits your brief.
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Nearest proof pattern.
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Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What was delivered on this engagement?
Volume transcribed: 500 hours. Languages: French Canadian, Russian, Persian. Accuracy: project-scoped quality review — spot-check peer review
What control kept the work stable?
Treating French Canadian as a separate pool rather than an accent of French is what preserved the properties the client was paying to capture.
Where should similar work go next?
Use AI data services for the delivery model, Speech data collection buyer guide for buyer-side evaluation, and the contact page for a scoped brief.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.