Case study
A hundred hours of Hindi-English bilingual speech for voice AI.
A voice AI program needed 100 hours of natural Hindi-English bilingual conversation, the kind of code-switching real speakers use but most datasets miss.
100 hours - Hindi and English (code-switching) - 20 bilingual
100 hours
Volume
20 bilingual
Speakers
Measured outcomes
Bilingual live-speech data
Project overview
What landed, and what made it hard.
A voice AI program needed 100 hours of natural Hindi-English bilingual conversation from 20 speakers, capturing the code-switching that real bilingual speakers use mid-sentence.
Delivery snapshot
Bilingual live-speech data
- Client
- A voice AI program (via a global LSP partner)
- Service
- Bilingual speech data collection
- Languages
- Hindi and English (code-switching)
- Volume
- 100 hours
- Speakers
- 20 bilingual
Why this mattered
Outcome before process.
Most speech datasets treat languages as separate; bilingual speakers do not, and a model trained on clean monolingual audio stumbles on real code-switching.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Bilingual speech data fails when speakers read scripted monolingual lines, when code-switching is edited out, or when audio quality varies across speakers.
The challenge
The problem to solve
The program needed natural code-switching conversation from genuinely bilingual speakers, captured to a consistent specification.
Operating response
What MoniSa changed
MoniSa sourced 20 genuinely bilingual speakers and captured natural conversation with code-switching intact, with QA on every recording for audio quality and acceptance.
- Genuine bilinguals Speakers were sourced for real Hindi-English fluency, not scripted monolingual reading.
- Natural code-switching Conversation captured the mid-sentence switching real speakers use, not edited monolingual lines.
- Per-recording QA Every recording was checked for audio quality and acceptance before delivery.
Results
Measured outcomes from this engagement.
The program received 100 hours of natural Hindi-English bilingual conversation from 20 speakers at full acceptance on this engagement, with code-switching preserved for model training.
| Volume | 100 hours |
|---|---|
| Languages | Hindi and English (code-switching) |
| Speakers | 20 bilingual |
| Quality | Full acceptance on this engagement |
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
Bilingual speech data needs genuinely bilingual speakers and natural code-switching, not scripted monolingual audio.
What decided the result
What decided the result
Preserving real code-switching mattered more than clean monolingual recordings.
What buyers can reuse
What buyers can reuse
- Voice models trained on monolingual audio stumble on the code-switching real bilingual speakers use.
- Genuine bilingual speakers and unedited natural conversation are what make code-switching data usable.
- The evidence keeps the client and partner details confidential and attributes the metrics only to this engagement.
Continue from this proof
Useful comparisons for the same problem.
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Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Hindi-English code-switching
- Bilingual conversation
- Voice AI training data
More proof
Related proof
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case evidence
Nearest proof pattern.
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Buyer questions
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Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What was delivered on this engagement?
Volume: 100 hours. Languages: Hindi and English (code-switching). Speakers: 20 bilingual
What control kept the work stable?
Preserving real code-switching mattered more than clean monolingual recordings.
Where should similar work go next?
Use AI data services for the delivery model, the case studies hub for buyer-side evaluation, and the contact page for a scoped brief.
Similar brief
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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
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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.