  Spiking neural networks (SNNs) with adaptive synapses reflect core properties
of biological neural networks. Speech recognition, as an application involving
audio coding and dynamic learning, provides a good test problem to study SNN
functionality. We present a simple, novel, and efficient nonrecurrent SNN that
learns to convert a speech signal into a spike train signature. The signature
is distinguishable from signatures for other speech signals representing
different words, thereby enabling digit recognition and discrimination in
devices that use only spiking neurons. The method uses a small, nonrecurrent
SNN consisting of Izhikevich neurons equipped with spike timing dependent
plasticity (STDP) and biologically realistic synapses. This approach introduces
an efficient and fast network without error-feedback training, although it does
require supervised training. The new simulation results produce discriminative
spike train patterns for spoken digits in which highly correlated spike trains
belong to the same category and low correlated patterns belong to different
categories. The proposed SNN is evaluated using a spoken digit recognition task
where a subset of the Aurora speech dataset is used. The experimental results
show that the network performs well in terms of accuracy rate and complexity.
