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
The project
Device voice data collection
- Client
- confidential voice AI buyer
- Service
- Voice data collection
- Languages
- 30
- Speakers
- 1,500 native speakers
The dataset had to reflect natural pronunciation variation rather than one narrow speaker profile per language.
The problem to solve
The buyer needed 50 unique speakers per language while maintaining audio clarity, script accuracy, and format compliance.
Accent and demographic balance had to be planned before recruitment, not corrected after recording.
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 |
What supported the result
Why the fit was real
The work needed controlled recruitment and language-level audio QA, not simple file collection.
What decided the result
Speaker diversity was treated as part of dataset quality from the beginning.
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.
Services and guides
Related services
Languages named
Examples referenced in the engagement.
- 20 Indian languages
- 10 international languages
- Device voice data
More proof
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Operating response
The change that was actually made.
Not the result — the decision taken mid-programme that the result depended on.
Recruited for variation
Speaker sourcing targeted natural spread in accent, pronunciation and speech pattern. A pool recruited for volume alone produces a dataset that sounds like one kind of person.
case evidence
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Buyer questions
Common questions.
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?
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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