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+ native linguists and AI data contributors · Founder-reported combined network · 4 Oct 2026
300+ languages across active service lines
4,500+ dialects and regional variants
110+ rare, indigenous and low-resource languages
1,000+ organizations served since 2015
Voice data recording visual: Rare-language translation surge handled across parallel pods.
Measured outcomes Voice data recording
150 hours Volume
Polish, Dutch, Australian English Languages
Strong first-pass acceptance Quality
10 per language Speakers

The project

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

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.

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

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.

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.

Volume150 hours
LanguagesPolish, Dutch, Australian English
QualityStrong first-pass acceptance
Speakers10 per language

What supported the result

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

A strong first-pass rate mattered because re-recording voice data is slow and costly.

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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Languages named

Examples referenced in the engagement.

  • Polish
  • Dutch
  • Australian English

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Buyer questions

Common questions.

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.

What happens if you cannot staff one of my language pairs?

For the proposed project, ask for pair-by-pair availability or a recruitment window in writing before agreeing a date. Define qualification and pilot approval for any new contributor before live work. A coverage claim should be checkable before the scope is signed.

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