ESP32 Offline Voice Recognition Using Edge Impulse

bread board

ESP32 Development Board

Arduino IDE 2.0 App

<html><body><h3>Project Description</h3><p>This project demonstrates how to build a fully offline voice-controlled system using the ESP32 microcontroller and Edge Impulse machine learning platform. The system performs on-device speech recognition without requiring internet connectivity, making it ideal for privacy-conscious applications and remote IoT deployments. </p><p>The voice assistant operates using a two-stage command structure: first detecting a wake word ("Marvin"), then listening for control commands ("on" or "off") to control connected devices. The system features dual confidence thresholds - 80% for reliable command execution and 50% for recognition feedback - ensuring accurate voice control with minimal false triggers. </p><p> </p><figure><img src="https://projects.arduinocontent.cc/ea52529d-9742-4bd3-8c92-0a2b237c0caf.png"/><figcaption>ESP32 offline voice recognition</figcaption></figure><p> </p><h3>Key Features</h3><ol><li><strong>Fully Offline Operation: </strong>No cloud dependency or internet connection required </li><li><strong>Custom Wake Word Detection: </strong>Trainable with custom voice commands </li><li><strong>Low Latency Response: </strong>On-device processing ensures a 200- 500ms response time </li><li><strong>Privacy-Focused: </strong>Audio data never leaves the device </li><li><strong>Visual Feedback System: </strong>LED patterns indicate system status and command recognition </li><li><strong>Expandable Architecture: </strong>Easy to add more voice commands and control functions </li><li><strong>Cost-Effective: </strong>Uses affordable ESP32 hardware and a free ML training platform </li></ol><p> </p><figure><img src="https://projects.arduinocontent.cc/675e507d-261b-4103-8671-d8b1de056662.png"/><figcaption>ESP32 voice recognition offline system with Edge Impulse</figcaption></figure><p> </p><h3>Hardware Components</h3><ol><li>ESP32 Development Board (240 MHz dual-core processor) </li><li>INMP441 MEMS Digital Microphone Module </li><li>2x LEDs (indicator and control) </li><li>2x 220Ω Resistors </li><li>Breadboard and jumper wires </li></ol><p> </p><figure><img src="https://projects.arduinocontent.cc/34649cfa-c7a8-4730-acd9-62ca3ec876d4.png"/><figcaption>Complete circuit diagram for ESP32 voice recognition offline</figcaption></figure><p> </p><h3>Software &amp; Tools</h3><ol><li>Arduino IDE </li><li>Edge Impulse Platform (for ML model training) </li><li>FreeRTOS (included with ESP32 core) </li><li>Custom dataset or Google Speech Commands Dataset </li></ol><p> </p><figure><img src="https://projects.arduinocontent.cc/0801e04d-cce2-4ac1-8ab5-ba4d03e94e2b.png"/><figcaption>Deploying ESP32 speech to text mode</figcaption></figure><p> </p><h3>How It Works</h3><ol><li><strong>Data Collection: </strong>Audio samples are collected for training (wake word and commands) </li><li><strong>Model Training: </strong>Edge Impulse platform trains a neural network on the dataset </li><li><strong>Model Deployment: </strong>Trained model is exported as an Arduino library </li><li><strong>Audio Processing: </strong>INMP441 microphone captures audio via I²S protocol at 16kHz </li><li><strong>Real-Time Inference: </strong>ESP32 runs ML model locally to recognise speech patterns </li><li><strong>Command Execution: </strong>Recognised commands trigger GPIO outputs to control devices </li></ol><p> </p><figure><img src="https://projects.arduinocontent.cc/198d6032-6b37-49a4-a0b6-f6d2273e1a22.png"/><figcaption>Edge Impulse login page</figcaption></figure><p> </p><h3>Technical Specifications</h3><ol><li>Sample Rate: 16 kHz </li><li>Recognition Type: On-device/Offline </li><li>Power Consumption: 160-260mA during active listening </li><li>Recognition Latency: 200-500ms total processing time </li><li>Model Accuracy: 85%+ (project-dependent on dataset quality) </li></ol><p> </p><h3>Applications</h3><ol><li>Voice-controlled home automation </li><li>Hands-free industrial control systems </li><li>Accessibility devices for disabled users </li><li>Smart office assistants </li><li>Privacy-sensitive IoT applications </li><li>Remote environment control systems </li></ol><p> </p><h3>Project Highlights</h3><p>This <a href="https://circuitdigest.com/microcontrollers-projects/esp32-offline-voice-recognition-using-edge-impulse">ESP32 voice recognition </a>showcases the power of edge AI for voice interfaces by combining machine learning, embedded systems, and real-time audio processing into a practical IoT solution. The framework is endlessly expandable - from controlling simple LEDs to integrating with home automation systems, industrial controls, or accessibility devices. </p><p></p></body></html>

ESP32 Offline Voice Recognition Using Edge Impulse

ESP32 voice assistant recognises commands offline using Edge Impulse ML, controls devices by voice, no cloud or internet needed.