Eliminating Bias in Enterprise Voice Assistants
Client
Aura Systems
Industry
NLP / Audio
Impact Deliverables
4 Key Results
Status
Delivered
The Challenge
Aura Systems identified a critical bias failure: their flagship voice assistant performed unacceptably poorly for users with heavy regional accents. They required an immediate influx of highly diverse, conversational audio data to retrain their ASR models and restore user trust.
Loopernode's Solution
Loopernode engineered a targeted global audio collection campaign, aggressively sourcing thousands of participants across 20 specific, hard-to-capture demographic and dialect groups. We captured natural, unscripted conversational audio across varied acoustic environments and delivered verbatim, timestamped transcriptions.
Key Business Results
Quantifiable breakthroughs achieved through Loopernode's dedicated data engineering pipeline.
Successfully collected and meticulously transcribed over 5,000 hours of unique audio.
Captured statistically significant representation across 20 distinct regional dialects.
Slashed Word Error Rate (WER) by an astonishing 30% for minority dialect speakers.
Directly contributed to a measurable spike in overall user engagement and retention.
Ready to achieve similar breakthroughs?
Partner with Loopernode to accelerate your AI datasets, custom annotation guidelines, and model performance.