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ESP32 offline voice recognition using Edge Impulse

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




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