Rithik Krisna
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ESP32 voice assistant recognizes offline commands using Edge Impulse ML, controls devices by voice, with no cloud or internet needed.
November 16, 2025
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Devices and components
ESP32 development board
Materials and tools
breadboard
Software and tools
Arduino IDE
Project description
Project description
Main features
Fully offline operation: no cloud dependency or internet connection required
Custom wake word detection: Trainable with custom voice commands
Low latency response: On-device processing ensures 200-500ms response time
Privacy-focused: audio data never leaves the device
Visual feedback system: LED patterns indicate system status and command recognition
Expandable architecture: easily add voice commands and additional control functions
Cost-effective: uses affordable ESP32 hardware and a free ML training platform
Hardware components
ESP32 development board (240 MHz dual-core processor)
INMP441 MEMS Digital Microphone Module
2x LED (indicator and control)
2x 220Ω resistors
Breadboard and connecting wires
Software and tools
Arduino IDE
Edge Impulse Platform (for training ML models)
FreeRTOS (included with ESP32 kernel)
Custom dataset or Google voice command dataset
How it works
Data collection: Audio samples are collected for training (wake word and commands)
Model Training: Edge Impulse platform trains a neural network on the dataset
Model Deployment: The trained model is exported as an Arduino library
Audio processing: INMP441 microphone captures audio via I²S protocol at 16kHz
Real-time inference: ESP32 runs ML model locally to recognize speech patterns
Command Execution: Recognized commands trigger GPIO outputs to control devices
Technical specifications
Sampling rate: 16kHz
Recognition type: on-device/offline
Power consumption: 160-260 mA active listening
Recognition latency: 200-500ms total processing time
Model accuracy: 85%+ (depending on project and dataset quality)
Applications
Voice-activated home automation
Hands-free industrial control systems
Accessibility Devices for Disabled Users
Smart office assistants
Privacy-aware IoT applications
Remote Environmental Control Systems
Project Highlights
GitHub repository
Downloadable files
Downloadable link
https://github.com/Circuit-Digest/ESP32-Voice-Control-Using-Edge-Impulse/archive/refs/heads/main.zip
Documentation
Downloadable link
https://github.com/Circuit-Digest/ESP32-Voice-Control-Using-Edge-Impulse/archive/refs/heads/main.zip
Note: Content and images are from: https://projecthub.arduino.cc/, with some modifications.
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