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We consider the ability of a very simple feed-forward neural network todiscriminate phonemes based on just relative power spectrum. The networkconsists of two neurons with symmetric nonlinear response over a spectralrange. The output of the neurons is subsequently fed to a comparator. We showthat often this is enough to achieve complete separation of data. We comparethe performance of found discriminants with that of more general neurons. Ourconclusion is that not much is gained in passing to real-valued weights. Morelikely higher number of neurons and preprocessing of input will yield betterdiscrimination results. The networks considered are directly amenable tohardware (neuromorphic) designs. Other advantages include interpretability,guarantees of performance on unseen data and low Kolmogorov complexity.

Phoneme discrimination using neurons with symmetric nonlinear response over a spectral range