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
The project
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
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.
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
Bilingual speech data fails when speakers read scripted monolingual lines, when code-switching is edited out, or when audio quality varies across speakers.
The program needed natural code-switching conversation from genuinely bilingual speakers, captured to a consistent specification.
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 |
What supported the result
Why the fit was real
Bilingual speech data needs genuinely bilingual speakers and natural code-switching, not scripted monolingual audio.
What decided the result
Preserving real code-switching mattered more than clean monolingual recordings.
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
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Services and guides
Related services
Languages named
Examples referenced in the engagement.
- Hindi-English code-switching
- Bilingual conversation
- Voice AI training data
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Buyer questions
Common questions.
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.
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.
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