Keyword spotting system with Nano 33 BLE sense using embedded machine learning approach

Abstract

Due to the obvious advancement of artificial intelligence, keyword spotting has become a fast-growing technology that was first launched a few years ago by hidden Markov models. Keyword spotting is the technique of finding terms that have been pre-programmed into a machine learning model. However, because the keyword spotting system model will be installed on a small and resource-constrained device, it must be minimal in size. It is difficult to maintain accuracy and performance when minimizing the model size. We suggested in this paper to develop a TinyML model that responds to voice commands by detecting words that are utilized in a cascade architecture to start or control a program. The keyword detection machine learning model was built, trained, and tested using the edge impulse development platform. The technique follows the model-building workflow, which includes data collection, preprocessing, training, testing, and deployment. 'On,' 'Off,' noise, and unknown databases were obtained from the Google speech command database V1 and applied for training and testing. The MFCC was used to extract features and CNN was used to generate the model, which was then optimized and deployed on the microcontroller. The model's evaluation represents an accuracy of 84.51% based on the datasets. Finally, the KWS was successfully implemented and assessed on Arduino Nano 33 BLE Sense for two studies in terms of accuracy at three different times and by six different persons.