11,500 hours of natural conversation across 20 languages
11,500+ hours of naturalistic conversational speech collected and transcribed at 98.3% verified accuracy.
Client Context & Operational Challenge
An AI company developing a multilingual voice assistant needed naturalistic conversational speech data across 20 languages — covering diverse speaker demographics, acoustic environments, and conversational styles. Existing speech corpora were predominantly read-speech and single-speaker, inadequate for training robust conversational ASR models.
Execution & Governance Model
Designed a scenario-driven collection methodology where paired speakers engaged in structured but unscripted conversations around provided topic prompts. Recruited speakers through regional talent networks with demographic targeting for age, gender, dialect, and accent diversity. Recording conducted through a quality-controlled mobile application with automated audio checks for noise level, clipping, and minimum duration. Transcription performed by native speakers with speaker turn annotation and diarization markup.
Scale & Velocity Constraints
- 20 languages across 4 major language families with diverse phonological systems
- Minimum 500 hours of audio per language with demographic balance requirements
- Naturalistic conversation scenarios — not scripted or read speech
- Acoustic environment diversity: indoor, outdoor, vehicle, public spaces
- Transcription accuracy requirements exceeding 98% with speaker diarization
What Was Delivered
Asset Outputs & Deliverables
- Collected and transcribed 11,500+ hours of conversational speech across 20 languages within a 6-month window. Demographic balance targets met for 18 of 20 languages. Transcription accuracy verified at 98.3% on independent audit samples. Corpus adopted as the primary training resource for the next-generation voice assistant model.
Operational Footprint
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