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.