60/day
audio files per annotator
100%
accuracy achieved
15 days
turnaround
20
annotators deployed
1
The Challenge
Amazon required large-scale audio evaluation and voice category labeling to improve their audio-based AI systems. Each audio file needed 50–100 granular annotations covering voice characteristics, tone classification, speech quality assessment, and categorical labeling. The project demanded perfect accuracy any mislabeling would propagate errors through downstream voice classification models.
- Maintaining 100% accuracy across 50–100 annotations per audio file
- Processing 60 audio files per annotator per day at consistently high quality
- Handling nuanced voice category distinctions requiring trained human judgment
- Delivering the complete dataset within a tight 15-day timeline
Client
Amazon
Industry
Voice AI / Audio Intelligence
Timeline
15 days
2
Our Approach
- Assembled a specialized 20-person team trained in audio evaluation and voice classification
- Developed detailed labeling guidelines covering voice categories, tone attributes, and quality signals
- Structured a high-throughput workflow enabling 60 files per annotator per day without quality compromise
- Built rapid calibration sessions to align annotator judgment on ambiguous audio samples
Quality Assurance
- Every audio annotation was independently reviewed by two different annotators
- Disagreements on voice category labels were escalated to senior QA reviewers
- Calibration checks were run daily to prevent labeling drift
- Only consensus-approved, double-verified annotations were delivered to Amazon
3
The Results
- Achieved 100% annotation accuracy across the entire dataset
- Processed 1,200 audio files per day across the 20-person team
- Delivered the complete project within the 15-day timeline
- Enabled Amazon to significantly improve their voice classification model performance
Quality Assurance
OUTCOME
Delivered a flawless audio evaluation dataset that directly enhanced Amazon's voice AI classification accuracy enabling production deployment of improved audio-based AI systems with zero annotation-driven errors.