Fixing a voice product that failed in real acoustic conditions
The voice interface worked in the lab but failed in kitchens, cars, and airports. We designed collection protocols for each of the 12 identified failure modes and cut error rates by 40%.
Client Context & Operational Challenge
A consumer electronics company discovered its voice interface performed poorly in non-ideal acoustic conditions — background noise, overlapping speech, accented commands, and far-field microphone input. Improving robustness required targeted audio collection covering specific failure modes identified through production error analysis.
Execution & Governance Model
Designed collection protocols for each failure mode: structured ambient noise injection at calibrated levels, multi-speaker overlap scenarios with controlled timing, accent-diverse command sets, and far-field recordings in 4 standardized room configurations. Recruited speakers specifically for accent diversity through regional casting calls. All sessions conducted in acoustically characterized environments with calibrated recording equipment.
Scale & Velocity Constraints
- 12 identified failure-mode categories each requiring targeted collection scenarios
- Controlled acoustic environment simulation for reproducible noise conditions
- Speaker accent diversity covering 30+ regional accents across 5 languages
- Far-field recording at multiple distances and room geometries
- Ground-truth transcription accuracy requirements of 99%+ for training data
What Was Delivered
Asset Outputs & Deliverables
- Collected 3,200+ hours of targeted audio across all 12 failure-mode categories. Speaker pool included 450+ individuals representing 35 regional accents across 5 languages. Model retraining on the collected data reduced voice interface error rates by an estimated 40% on the targeted failure modes. Collection methodology documented as a reusable protocol for ongoing robustness testing.
Operational Footprint
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