Scaling Multilingual Voice Data for AI
How an African data operations partner supported a large-scale transcription programme across multiple markets
MyDeepTech Team · Case Study · 5 min read
- Multi-market delivery — contributors recruited and coordinated across multiple African and international markets.
- End-to-end workflow — recruitment, onboarding, transcription, quality review and performance management, run as one pipeline.
- Continuous quality control — treated as an ongoing process, not a final-stage check.
- Built to scale — a repeatable operational layer for multilingual, multi-dialect voice data.
The Challenge
A global AI company was expanding its speech and voice capabilities across diverse markets and needed high-quality transcription data to support the development and evaluation of its AI systems.
The project required more than simply converting speech into text. Audio data had to be handled consistently across different languages, accents, dialects, and regional varieties while maintaining strict quality standards.
The scale of the programme also required a distributed workforce that could be onboarded quickly and managed effectively across multiple markets.
Our Approach
MyDeepTech supported the programme through a structured transcription workflow built around local-language talent.
We recruited and coordinated contributors with relevant language and regional knowledge, then organised them into project teams based on the required language coverage.
The workflow included:
- Contributor recruitment and screening
- Language and project onboarding
- Transcription of voice recordings
- Quality review and correction
- Performance monitoring
- Task allocation and deadline management
- Ongoing communication with contributors
- Escalation and resolution of quality issues
Rather than relying on a single centralised workforce, the programme used contributors across multiple African and international markets. This allowed the operation to support a broad range of languages and regional speech patterns.
One of the key requirements was access to contributors who understood the way people actually speak in their respective markets.
Building Local-Language Capacity
One of the key requirements was access to contributors who understood the way people actually speak in their respective markets.
Our teams included speakers working across African languages and regional varieties of widely spoken languages. This helped provide coverage that would be difficult to achieve through a single-language or single-market workforce.
The operational model allowed us to onboard contributors in batches, train them against project requirements, monitor their performance and scale the workforce as demand changed.
Quality Control
Quality was managed as an ongoing process rather than a final-stage check.
Contributors received project-specific guidance before beginning production. Their work was monitored throughout the project, with additional review applied where required.
Performance tracking allowed the team to identify:
- Contributors requiring additional support
- Recurring transcription errors
- Productivity issues
- Tasks requiring additional review
- Language-specific quality patterns
This created a feedback loop between production, review and contributor management.
The Result
The programme demonstrated that a distributed African workforce could be organised to support large-scale voice-data operations while maintaining structured quality controls.
The project enabled the client to access a broader pool of local-language talent and provided a scalable operational layer for speech-data collection and transcription.
Why It Matters
AI systems increasingly need data that reflects how people actually communicate, not only standardised or widely represented languages.
Our work demonstrates an approach to building that data layer by combining:
Local-language talent + structured operations + quality control + scalable workforce management.
For organisations developing speech, voice or multilingual AI systems, this model can provide access to diverse language data while maintaining a consistent operational framework.
AI Data. Human Intelligence.
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