Voice-controlled wake-up assistant with weather integration.
A premium home electronics brand looking for a privacy-focused bedside alarm clock with local voice control.
Implementing reliable wake-word detection and basic intent recognition without relying on constant cloud connectivity, respecting user privacy in the bedroom.
The ESP32-S3 (Dual-core Xtensa LX7 @ 240MHz with vector instructions) was selected for its balance of Wi-Fi capabilities and neural network acceleration. It drives a custom segment-LCD and an I2S Class-D amplifier.
We utilized the ESP-SR speech recognition framework. Using TensorFlow Lite for Microcontrollers (TinyML), we trained a customized 400KB acoustic model for 15 offline voice commands. The device only connects to Wi-Fi to fetch weather and time sync, keeping the microphone data strictly local.
Wake-word detection accuracy > 95% at 3 meters. Zero cloud audio transmission ensures 100% privacy compliance. Total system power consumption is under 1W.
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