Case study
A hundred and fifty hours of voice data, with a strong first-pass acceptance rate.
A speech program needed 150 hours of clean voice recordings across Polish, Dutch, and Australian English, captured to spec so none of it bounced back in QA.
150 hours - Polish, Dutch, Australian English - Strong first-pass acceptance
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
Voice data recording
Project overview
What landed, and what made it hard.
A speech program needed 150 hours of voice recordings across Polish, Dutch, and Australian English, delivered through a top-100 LSP, with strict device-level audio specifications.
Delivery snapshot
Voice data recording
- Client
- A speech program (via a top-100 LSP)
- Service
- Voice data recording
- Languages
- Polish, Dutch, Australian English
- Volume
- 150 hours
- Quality
- Strong first-pass acceptance
Why this mattered
Outcome before process.
Voice data is expensive to re-record: a sample that fails QA means re-booking a speaker, so the cost of getting capture right the first time is high.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Voice recording fails when speakers drift from the script, when audio quality varies across contributors, or when format compliance is checked only at the end.
The challenge
The problem to solve
The program needed every sample to meet the specification on the first pass, across three languages and a roster of speakers.
Operating response
What MoniSa changed
MoniSa sourced ten speakers per language and ran QA on every recording for script accuracy, audio clarity, and format compliance before submission.
- Per-recording QA Every sample was checked for script accuracy, audio clarity, and format before it was submitted.
- Speaker roster Ten speakers per language gave the program voice diversity within a consistent spec.
- First-pass discipline Catching issues before submission meant fewer re-record cycles on the delivered set.
Results
Measured outcomes from this engagement.
The program received 150 hours of voice recordings across three languages with a strong first-pass acceptance rate on this engagement.
| Volume | 150 hours |
|---|---|
| Languages | Polish, Dutch, Australian English |
| Quality | Strong first-pass acceptance |
| Speakers | 10 per language |
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
Voice capture needs per-recording QA before submission, not a bulk delivery that bounces back for re-records.
What decided the result
What decided the result
A strong first-pass rate mattered because re-recording voice data is slow and costly.
What buyers can reuse
What buyers can reuse
- Voice data economics turn on the first-pass rate: re-records mean re-booking speakers.
- Per-recording QA before submission is what keeps the accepted set clean.
- The evidence keeps the client and partner details confidential and attributes the metrics only to this engagement.
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.
- Polish
- Dutch
- Australian English
More proof
Related proof
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case evidence
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: 150 hours. Languages: Polish, Dutch, Australian English. Quality: Strong first-pass acceptance
What control kept the work stable?
A strong first-pass rate mattered because re-recording voice data is slow and costly.
Where should similar work go next?
Use AI data services for the delivery model, the case studies hub 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.