Custom Wake Word Detector
Designed and trained a custom wake word detection model optimized for low-latency, on-device inference.
Impact
10,000+
Condidates Screened Monthly usding AI
$400,000+
Annual savings on recruitment costs
30%
Improvement in EBITDA margins
The system was built to deliver reliable voice activation performance under real-world acoustic conditions, including noisy environments and variable speech patterns.
The model architecture, training pipeline, and evaluation strategy were developed to prioritize detection accuracy while minimizing false activations. Compared to Porcupine, the solution achieved improved wake word recognition performance, providing more stable activation behavior and reduced false triggers.
This project involved model design, dataset curation, training optimization, and inference performance tuning for production-style deployment scenarios.
Impact
Designed and trained a custom wake word detection model
Achieved improved detection accuracy compared to Porcupine
Reduced false activations and unintended triggers
Lowered missed wake events under real-world usage conditions
Optimized for low-latency, on-device inference
Maintained robust performance in noisy acoustic environments
Enabled reliable offline voice activation
Improved overall responsiveness of the voice interaction system
Built a reusable training and evaluation pipeline
FP was less than 1 in 6 hours
The Challenge
Wake word detection systems must operate with high accuracy under strict real-time constraints. However, existing solutions often struggle with:
High false activation rates (triggering unintentionally)
Missed wake events in noisy environments
Latency issues affecting user experience
Limited adaptability to custom wake words
Dependence on cloud processing instead of on-device inference
The goal was to build a highly accurate, low-latency wake word detection system that performs reliably in real-world conditions.
Approch
We designed a custom deep learning–based audio model optimized for edge deployment.
Key approach elements:
Building a tailored model architecture for wake word detection
Curating and augmenting a diverse dataset (noise, accents, environments)
Designing a training pipeline focused on minimizing false positives and false negatives
Benchmarking performance against Porcupine
Optimizing inference for low-latency, on-device execution
Iteratively refining thresholds and detection logic for stability
The objective was to outperform existing solutions while ensuring production-ready performance.
Solution
We developed a robust wake word detection system with:
Custom-Trained Detection Model
Tailored specifically for the target wake word and use caseLow-Latency On-Device Inference
Enables instant response without relying on cloud processingNoise-Resilient Performance
Maintains accuracy in real-world acoustic environmentsFalse Trigger Reduction System
Minimizes unintended activations while preserving sensitivityReusable Training Pipeline
Allows easy retraining and adaptation for new wake words
Result
Delivered stable and reliable voice activation performance
Outperformed baseline solutions in detection accuracy
Achieved strong real-world robustness across varied environments
Enabled seamless integration into voice-enabled systems
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