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
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.
How AI data services was delivered
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.
What was delivered on 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 this team fit the assignment
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.
Work connected to Device voice data collection
Related services, buying guidance and language coverage
AI data services and buyer guidance
Languages listed
- 20 Indian languages
- 10 international languages
- Device voice data
More proof
Compare this case with Compressed audio collection and Maithili ASR transcription to judge whether the operating pattern fits your brief.
What changed in the work
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.
Compare Device voice data collection with other projects
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What we did. MoniSa paired native linguists with synchronized transcription and JSON export workflow.
The result. The buyer received structured ASR data instead of a flat transcript cleanup burden.
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Device voice data collection questions
What was delivered on this engagement?
Languages: 30. Speakers: 1,500. Speaker target: 50 per language
Which control supported the Device voice data collection result?
Speaker diversity was treated as part of dataset quality from the beginning.
Scope work like Device voice data collection
For a comparable project, include the language mix, review requirements, deadline and evidence your team needs.
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