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
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered 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
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 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 QAEvery sample was checked for script accuracy, audio clarity, and format before it was submitted.
- Speaker rosterTen speakers per language gave the program voice diversity within a consistent spec.
- First-pass disciplineCatching 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.
Voice capture needs per-recording QA before submission, not a bulk delivery that bounces back for re-records.
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.
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Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Polish
- Dutch
- Australian English
More proof
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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
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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 approval