Fig. 5: Evaluation of the Speech Commands recognition (ten commands) by the polariton neuromorphic network.

a The recognition accuracy in dependence on the size of the input signal lattice \({n}_{{\rm{in}}}\) (lower scale) or the number of neurons (dyads) in the hidden layer \({N}_{{\rm{d}}}\) (upper scale). b The recognition accuracy in dependence of the densing degree \(s\) of the input signal for square polariton lattice systems of different size \({n}_{{\rm{in}}}\) with different numbers of neurons in the interaction layer: \({n}_{{\rm{in}}}=20\) with 220 neurons (blue), \({n}_{{\rm{in}}}=28\) with 420 neurons (green), \({n}_{{\rm{in}}}=39\) with 800 neurons (red), and \({n}_{{\rm{in}}}=160\) with 12960 neurons (brown). Red markers indicate the maxima of the dependencies. Horizontal dashed lines indicate the accuracy levels for alternative classification approaches (from bottom to top): the linear software classification of the binarized (49.2%) and non-binarized (61%) MFCC feature matrices, and the HMM-GMM-based classification of the MFCC feature matrices (65.8%)