Conversational ASR data in Maithili.
An ASR team needed conversational Maithili transcription with timestamps, segmentation, and structured JSON output.
Maithili - ~20 hours - Reviewed per engagement rules
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
Measured outcomes
Maithili ASR transcription
The project
Maithili ASR transcription
- Client
- confidential speech AI buyer
- Service
- Conversational ASR transcription
- Language
- Maithili
- Volume
- ~20 hours
The audio included multi-speaker conversation, dialectal variation, fillers, hesitations, slang, and background events.
The problem to solve
Maithili has limited digital resources and a limited trained transcription pool.
The buyer needed speech represented faithfully enough for ASR training, not cleaned into unnatural written language.
What MoniSa changed
MoniSa built a custom transcription workflow for synchronized playback, segmentation, timestamping, and JSON export.
Native linguists
A native-linguist team handled conversation detail with backup support available.
Tooling fit
The workflow supported playback, segmentation, timestamps, and structured export.
Continuous QA
Review cycles improved consistency as recurring transcription patterns appeared.
Results
Measured outcomes from this engagement.
~20 hours of conversational audio were transcribed with structured JSON output ready for ASR training pipelines.
| Language | Maithili |
|---|---|
| Audio | ~20 hours |
| Quality after review | Reviewed per engagement rules |
| Output | Structured JSON |
What supported the result
Why the fit was real
The engagement needed native-language judgment and workflow tooling in the same delivery path.
What decided the result
The output preserved conversation features that ASR teams need but generic transcription often removes.
What buyers can reuse
- Low-resource ASR work needs tooling and native review, tooling and native review before transcript volume.
- Structured output reduced buyer-side cleanup before model ingestion.
- Accuracy language is scoped to this engagement only.
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.
- Maithili
- Conversational audio
- Structured JSON
More proof
Related projects
Compare this case with Multilingual audio intelligence and Audio transcription standing operation to judge whether the operating pattern fits your brief.
case evidence
Related projects.
These related cases keep the next click close to the same kind of work.
AI output reviewGuardrails prompts analyzed with language-specific safety context.
AI guardrails dataset
The challenge. An AI safety team needed prompt analysis that preserved Indian-language nuance.
What we did. MoniSa trained resources on the taxonomy and calibrated sensitive examples by language.
The result. The buyer received safety-prompt data organized for model-training use.
Recognise your own project in one of these?
Send the language list and volumeTranslation 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.
Localization servicesA 100+ title game catalogue localized across 7 languages with in-game text kept in place.
Game localization at title scale
Problem. A games program needed 100+ titles in 7 languages without translated text breaking fixed UI layouts.
Action. MoniSa localized each title to its in-game space, managing text expansion and contraction per title.
Result. More than 100 titles localized across 7 languages with menus, buttons, and dialogue intact.
Buyer questions
Common questions.
What was delivered on this engagement?
Language: Maithili. Audio: ~20 hours. Quality after review: Reviewed per engagement rules
What control kept the work stable?
The output preserved conversation features that ASR teams need but generic transcription often removes.
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.
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.
Send a brief