Case study
Conversational ASR data in Maithili.
An ASR team needed conversational Maithili transcription with timestamps, segmentation, and structured JSON output.
Maithili - ~20 hours - reviewed quality
110,000+
verified language specialists
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered since 2015
Measured outcomes
Maithili ASR transcription
Project overview
What landed, and what made it hard.
An ASR team needed conversational Maithili transcription with timestamps, segmentation, and structured JSON output.
Delivery snapshot
Maithili ASR transcription
- Client
- confidential speech AI buyer
- Service
- Conversational ASR transcription
- Language
- Maithili
- Volume
- ~20 hours
Why this mattered
Outcome before process.
The audio included multi-speaker conversation, dialectal variation, fillers, hesitations, slang, and background events.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Maithili has limited digital resources and a limited trained transcription pool.
The challenge
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.
Operating response
What MoniSa changed
MoniSa built a custom transcription workflow for synchronized playback, segmentation, timestamping, and JSON export.
- Native linguistsA native-linguist team handled conversation detail with backup support available.
- Tooling fitThe workflow supported playback, segmentation, timestamps, and structured export.
- Continuous QAReview 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 quality |
| Output | Structured JSON |
Selection logic
What protected the result.
The engagement needed native-language judgment and workflow tooling in the same delivery path.
Why the fit was real
Why the fit was real
The engagement needed native-language judgment and workflow tooling in the same delivery path.
What decided the result
What decided the result
The output preserved conversation features that ASR teams need but generic transcription often removes.
What buyers can reuse
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.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Maithili
- Conversational audio
- Structured JSON
More proof
Related proof
Compare this case with Multilingual audio intelligence and Audio transcription standing operation to judge whether the operating pattern fits your brief.
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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?
Language: Maithili. Audio: ~20 hours. Quality after review: reviewed quality
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.
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