Spoken-Digit Recognition Without Training: Geometry, Coupling, and Drive Effects in Oscillator Networks

Coupled-oscillator networks are gaining interest as a machine-learning substrate, on the premise that oscillator physics turns signals into useful structure. Yet the ablations that would isolate what the oscillator dynamics contribute are rarely run. We run them for untrained coupled oscillator networks, using spoken-digit classification on AudioMNIST as the measure. A small untrained network is read by a closed-form linear readout and compared with five trained conventional networks (GRU, TCN, CNN, transformer and S4D) matched in parameters and with three controls: the same network with its coupling removed, a non-oscillating bank of leaky integrators with the same states and parameters, and the same readout fitted directly to the network's input. Every experimental arm's state is summarized by the same time-pooled statistics and spoken digits classified using the same number of features, so that readout capacity cannot mask dynamics. The ablations, eight experiments of 6,353 runs in all, vary six coupling functions, eight lattice geometries, and the natural frequencies, input pathway, input gain, readout width and training set size, on digit recognition and on a temporal order task that order-blind readouts cannot solve. The design separates three explanations for an untrained network's accuracy: its input representation, the width of its readout, and the oscillator dynamics and structure itself. Our ablation study results give future studies of trained oscillator networks an untrained baseline to compare against for speech recognition tasks, and insight into which factors contribute to an oscillator network's effectiveness on time series tasks.