Case study
Device voice data across 30 languages.
A device voice-recognition team needed balanced speaker data across 30 languages with demographic and accent diversity.
30 - 1,500 - 50 per language
30
Languages
1,500
Speakers
50 per language
Speaker target
Measured outcomes
Device voice data collection
Project overview
What landed, and what made it hard.
A device voice-recognition team needed balanced speaker data across 30 languages with demographic and accent diversity.
Delivery snapshot
Device voice data collection
- Client
- confidential voice AI buyer
- Service
- Voice data collection
- Languages
- 30
- Speakers
- 1,500 native speakers
Why this mattered
Outcome before process.
The dataset had to reflect natural pronunciation variation rather than one narrow speaker profile per language.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
The buyer needed 50 unique speakers per language while maintaining audio clarity, script accuracy, and format compliance.
The challenge
The problem to solve
Accent and demographic balance had to be planned before recruitment, not corrected after recording.
Operating response
What MoniSa changed
MoniSa sourced speakers by language, accent, and demographic fit, then applied standardized recording guidelines and QA checks.
- Speaker balancing Recruitment targeted natural variation in pronunciation, accent, and speech pattern.
- Recording QA Each recording was checked for script accuracy, audio clarity, format, and noise.
- Language-level control The team tracked each language separately so one language could not mask another.
Results
Measured outcomes from this engagement.
1,500 speakers were recorded across 30 languages, giving the buyer balanced device-level voice data.
| Languages | 30 |
|---|---|
| Speakers | 1,500 |
| Speaker target | 50 per language |
| End use | Device voice recognition and assistant training |
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 controlled recruitment and language-level audio QA, not simple file collection.
What decided the result
What decided the result
Speaker diversity was treated as part of dataset quality from the beginning.
What buyers can reuse
What buyers can reuse
- Voice data quality starts with speaker design before recording cleanup.
- Language-level tracking kept the dataset balanced across the full program.
- The client and device program remain confidential in buyer-facing copy.
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.
- 20 Indian languages
- 10 international languages
- Device voice data
More proof
Related proof
Compare this case with Compressed audio collection and Maithili ASR transcription to judge whether the operating pattern fits your brief.
case evidence
Nearest proof pattern.
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Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
What was delivered on this engagement?
Languages: 30. Speakers: 1,500. Speaker target: 50 per language
What control kept the work stable?
Speaker diversity was treated as part of dataset quality from the beginning.
Where should similar work go next?
Use AI data services for the delivery model, AI data annotation vendor guide for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.
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 approval