Multi-Dialect ASR Feedback Loop & Acoustic Normalization for Field Logistics
Client identifiers anonymized under strict Non-Disclosure Agreements (NDAs). Performance metrics reflect architectural benchmark simulations.
About the Project
This voice recognition platform was engineered for a multinational logistics and supply chain operator deploying voice-directed picking systems across 14 distribution centers worldwide.
Warehouse pickers wear ruggedized headsets to receive vocal bin assignments and speak SKU verification codes, shelf locations, and box counts directly into an automated warehouse management system (WMS).
Challenges We Faced
1. 85dB+ Acoustic Industrial Noise Environments
Fulfillment centers operate forklifts, automated conveyor belts, and pneumatic sorters that create over 85dB of background noise, causing standard ASR models to drop single-digit numbers or mishear confirmation commands in noisy aisles.
2. High Error Rates on Regional Dialects and Accents
Warehouse teams speak diverse regional dialects and non-native accents. The baseline speech model suffered an unacceptable 28% error rate on accented numeric confirmation commands, causing picking delays and inventory discrepancies.
3. Resource Constraints on Mobile Wearable Terminals
Headsets were tethered to low-power Android mobile computers (Zebra handhelds) with limited battery budgets, restricted compute capabilities, and intermittent Wi-Fi coverage across large warehouse facilities.
Solution Architecture
Acadify engineered a hybrid edge-cloud voice system combining neural noise filtering with automated dialect feedback loops, delivered through our product engineering practice.
We deployed a low-overhead neural noise suppression pre-filter on the handheld terminal to eliminate acoustic hum before inference, paired with a quantized on-device acoustic model that handles numerical commands offline.
- Edge Neural Noise Suppression: Integrated an optimized RNNoise pre-filter on the Android handheld, stripping machinery hum and forklift rumble in real time while maintaining sub-15ms processing overhead on device.
- Quantized On-Device Numeric Engine: Deployed an 8-bit quantized Conformer-CTC model using ONNX Runtime Mobile directly on the handheld, enabling instant numeric and bin confirmation even during total Wi-Fi dropouts.
- Confirmation-Tone Feedback Calibration: When a picker receives an incorrect confirmation tone and repeats a command ('Correction: Bin 42'), the system automatically flags the discrepancy and logs the raw audio pair to calibrate facility-specific phonetic confusion matrices.
Why Acadify's Engineering Approach Fit the Project
Industrial edge AI deployments must operate reliably in hostile physical environments—harsh noise, low-power mobile hardware, and diverse global workforces. Acadify built an edge-native speech platform that combines neural noise suppression with automated dialect adaptation to keep logistics lines running at maximum efficiency.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's AI Development and Data Annotation Services practices.