Design Of Emergency Keyword Recognition Using Arduino Nano BLE Sense 33 And Edge Impulse

Abstract

This project focuses on Custom Keyword Voice Recognition (CKVR) for emergency response scenarios. A multilingual keyword spotting system is developed using the Arduino Nano 33 BLE Sense board and Edge Impulse. The system accurately recognizes the keyword "help" in English, Arabic, Kurdish, and Malay languages. The project utilizes Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and employs deep learning techniques for model training. By optimizing the model through quantization and achieving 100% accuracy in training and testing phases, the system provides a reliable solution for identifying emergency keywords. The developed system has the potential to enhance safety in public spaces such as malls, hospitals, schools, and stations by quickly responding to individuals in distress. The project demonstrates the effectiveness of the chosen approach, highlighting the significance of MFCC processing, classification learning, and optimized model design in speech recognition. The successful development of this keyword spotting system opens doors for further advancements in the field and emphasizes the potential for innovative solutions that contribute to a safer and more responsive world.