Mel-frequency cepstral coefficients feature extracted voice recognition task using atomic switch Ag/Ag₂S device-based time-delayed reservoir computing

Abstract

Neuromorphic devices have diverse potential applications, such as brain-inspired computers and promising high-performance arithmetic systems with power saving. Reservoir computing (RC), a type of recurrent neural network (RNN), achieves learning by adjusting the weights between the intermediate and output layers. Time-delay reservoir computing introduces a delay and creates virtual nodes within the middle layer. The Ag/Ag₂S nanoparticles function as nonlinear electrical devices, following the atomic switch principles of the time-delay system. Voice recognition was performed with 87.81% accuracy when six different people pronounced the same number, and 80.18% when the same person pronounced ten different numbers.