Speech Recognized by Cavity Magnon Polaritons

Abstract

Reservoir computing, distinguished by its reduced training overhead compared to traditional recurrent neural networks, emerges as a proficient architecture for neural networks. Here, an innovative reservoir computing (RC) paradigm based on cavity magnonics for a speech recognition task is demonstrated, which leverages the high tunability, low energy consumption, and fast response of cavity magnon polaritons (CMPs). The audio signal is filtered into a pulse train and then input into the CMP device to rapidly alter the nonlinear dynamics of the CMPs. Due to the short-term memory effect and nonlinear response of the CMP device, virtual nodes are generated in the time domain, forming a recurrent neural network. By maximizing the nonlinear coefficient of the CMP device, exceptional speech recognition accuracy is achieved, with an error rate of less than 0.8%. This work heralds a new era for magnon-based reservoir computing, promising enhanced capabilities for addressing complex temporal tasks in the future.