Neuromorphic Speech Recognition With Photonic Convolutional Spiking Neural Networks

Abstract

Spiking neural network (SNN) have attracted lots of attention due to its event-driven nature and powerful computation capability. However, it is still limited to simple task due to the training difficulty. In this work, we propose a hybrid architecture of photonic convolutional spiking neural network (PCSNN) to realize the speech recognition task. In the PCSNN, the feature extraction is realized by a convolution SNN with unsupervised learning algorithm, the classification is realized by a photonic SNN with modified time-based supervised training algorithm. The TIDIGITS dataset is used to test the speech recognition performance of the proposed PCSNN, and the highest testing accuracy is 93.75%. The proposed PCSNN provides a solution for architecture and algorithm co-design for the speech recognition task, which is helpful for extending the applications of photonic SNN.