Case study
Conversational ASR data in Maithili.
An ASR team needed conversational Maithili transcription with timestamps, segmentation, and structured JSON output.
Maithili - ~20 hours - project-scoped quality review
110,000+
verified language specialists
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served 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
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 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 | project-scoped quality review |
| Output | Structured JSON |
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 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.
case evidence
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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: project-scoped quality review
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 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.