A commercial restaurant kitchen during dinner service is an acoustic nightmare. Sizzling grills, clanging pans, shouted orders, running water, exhaust fans, background music from the dining room — all blending into a wall of noise that can exceed 90 dB. This is where our voice engine lives and thrives.
The Problem with Standard ASR
Standard automatic speech recognition (ASR) models are trained on clean audio — podcasts, audiobooks, phone calls in quiet rooms. When exposed to restaurant-level noise, their accuracy drops from 95%+ to below 70%. In a noisy kitchen, traditional ASR is effectively unusable. This isn't a minor degradation — it's a complete system failure.
Our Approach: Multi-Stage Noise Separation
Rather than trying to build a single model that handles all acoustic conditions, we developed a multi-stage pipeline where each stage specializes in a different aspect of the noise problem.
“We don't filter noise — we teach the system to hear through it.”
- 01Stage 1: Acoustic Scene Classification — Identifying the type of noise environment in real-time
- 02Stage 2: Adaptive Beamforming — Dynamically focusing on the speech source
- 03Stage 3: Neural Noise Separation — Deep learning-based source separation trained on restaurant audio
- 04Stage 4: Robust ASR — Fine-tuned recognition model that expects and handles residual noise
The result is a system that doesn't just tolerate noise — it thrives in it. And it gets better over time, as every call in a noisy environment adds to our proprietary training dataset. This is the kind of compounding advantage that becomes a genuine moat.
Building the operating system for physical spaces.