  Deep Neural Networks (DNNs) are the current state-of-the-art models in many
speech related tasks. There is a growing interest, though, for more
biologically realistic, hardware friendly and energy efficient models, named
Spiking Neural Networks (SNNs). Recently, it has been shown that SNNs can be
trained efficiently, in a supervised manner, using backpropagation with a
surrogate gradient trick. In this work, we report speech command (SC)
recognition experiments using supervised SNNs. We explored the
Leaky-Integrate-Fire (LIF) neuron model for this task, and show that a model
comprised of stacked dilated convolution spiking layers can reach an error rate
very close to standard DNNs on the Google SC v1 dataset: 5.5%, while keeping a
very sparse spiking activity, below 5%, thank to a new regularization term. We
also show that modeling the leakage of the neuron membrane potential is useful,
since the LIF model outperformed its non-leaky model counterpart significantly.
