Speech Dataset Collection for Multilingual Voice AI Training
Challenges:
The client needed multilingual conversational speech data featuring natural code-switching across South Asian, Latin-American, and European languages. Lack of authentic mixed-language datasets and diversity led to low AI model accuracy and biased speech recognition outcomes.
Industry:
Artificial Intelligence / Data Annotation / Speech Technology
Solutions:
SummitNext designed a global collection framework that recruited real code-switching speakers, promoted natural delivery, and implemented rapid QA validation workflows for large-scale multilingual audio capture.
Results:
Delivered over 520+ validated hours of multilingual conversational data with a 95% acceptance rate and near-zero rework, leading the client to extend the contract to five additional Southeast Asian markets.
About the Client
The client is a global speech-AI company focused on improving voice recognition models for multilingual users worldwide.
Their goal was to build diverse, realistic datasets that accurately represent the way people naturally blend languages in everyday speech—especially across Asia, Europe, and Latin America.
Case Overview
SummitNext partnered with the client to deliver an end-to-end multilingual speech dataset collection project. The engagement focused on capturing natural, unscripted, code-switched conversations under strict demographic and technical guidelines. The solution included recruiting real-world bilingual and multilingual speakers, training participants for natural delivery, and embedding real-time quality validation to maintain dataset integrity. This initiative bridged critical gaps in multilingual voice AI training and improved the inclusivity and accuracy of future speech recognition systems.
Challenges
Limited availability of authentic, code-switched speech data across multiple regions
Lack of diversity in age, accent, and dialect representation.
Participants’ tendency to over-rehearse or suppress natural accents.
Slow validation cycles in traditional data collection workflows.
Solution:
SummitNext implemented a three-phase execution model emphasizing authenticity, diversity, and speed:
- Freelancer Base for Real-World Code-Switching – Recruited active code-switchers (urban youth, customer service agents, influencers, gig workers) through campus events, digital platforms, and local community networks. Screened participants for language fluency and accent balance.
- Educate for Natural Delivery – Conducted onboarding sessions and live Q&As to encourage authentic, informal speech patterns. Shared reference guides addressing tone, slang, and accent variations to reduce participant anxiety and ensure realistic recordings.
- Deliver Quality & Feedback Fast – Embedded real-time AI-assisted audio validation for instant error detection. Implemented transparent scoring systems, contributor dashboards, and rapid feedback loops to improve data quality and turnaround speed.
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WHO WE ARE
We at SummitNext Technologies, founded in 2020, are a BPO company with a vision to transform customer support, customer acquisition, data annotation and backend support domains through technology, human expertise, and innovation. We are Head Quartered in Malaysia, with offices in Philippines. India and Uzbekistan. We are sup
ported with Remote teams in more than 28+ countries.
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