  We consider the ability of a very simple feed-forward neural network to
discriminate phonemes based on just relative power spectrum. The network
consists of two neurons with symmetric nonlinear response over a spectral
range. The output of the neurons is subsequently fed to a comparator. We show
that often this is enough to achieve complete separation of data. We compare
the performance of found discriminants with that of more general neurons. Our
conclusion is that not much is gained in passing to real-valued weights. More
likely higher number of neurons and preprocessing of input will yield better
discrimination results. The networks considered are directly amenable to
hardware (neuromorphic) designs. Other advantages include interpretability,
guarantees of performance on unseen data and low Kolmogorov complexity.
