Fig 1. Architecture of proposed spiking neural network (SNN).

The network consists of an input layer, a convolutional layer, and a pooling layer. The input layer converts the Mel-Frequency Spectral Coefficients (MFSC) of speech signal into spikes using the time-to-first-spike coding scheme. The convolutional layer contains multi features maps which are responsible for detecting different features, and their input weights are learned by spike-timing-dependent plasticity (STDP). Each feature map in the convolutional layer is divided into non-overlapping sections which have shared input weights. The pooling layer compresses the output of the convolutional layer, and its output is classified by a linear classifier.