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.
These related cases keep the next click close to the same kind of work.
AI data servicesLow-resource ASR data moved into structured training output.
Maithili ASR transcription
The challenge. A speech AI buyer needed Maithili conversation captured with training-ready structure.
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.
AI output reviewGuardrails prompts analyzed with language-specific safety context.
AI guardrails dataset
Problem. An AI safety team needed prompt analysis that preserved Indian-language nuance.
Action. MoniSa trained resources on the taxonomy and calibrated sensitive examples by language.
Result. The buyer received safety-prompt data organized for model-training use.
Translation servicesAutomotive content localized across a rare pair with terminology built from scratch.
Automotive localization, rare pair
Problem. A luxury automotive manufacturer needed German-to-Kazakh manuals and marketing where no established automotive terminology existed.
Action. MoniSa built the domain glossary first, then translated and reviewed manuals and marketing against it.
Result. 500,000 words delivered across a rare pair with terminology held consistent for safety-critical content.
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?
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.
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 approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.