  Spiking neural networks (SNN) are a promising research avenue for building
accurate and efficient automatic speech recognition systems. Recent advances in
audio-to-spike encoding and training algorithms enable SNN to be applied in
practical tasks. Biologically-inspired SNN communicates using sparse
asynchronous events. Therefore, spike-timing is critical to SNN performance. In
this aspect, most works focus on training synaptic weights and few have
considered delays in event transmission, namely axonal delay. In this work, we
consider a learnable axonal delay capped at a maximum value, which can be
adapted according to the axonal delay distribution in each network layer. We
show that our proposed method achieves the best classification results reported
on the SHD dataset (92.45%) and NTIDIGITS dataset (95.09%). Our work
illustrates the potential of training axonal delays for tasks with complex
temporal structures.
