Volterra series for analyzing MLP based phoneme posterior estimator
Joel Pinto, Garimella S. V. S. Sivaram, Hynek Heřmanský, Mathew Magimai.-Doss · 2009
We present a framework to apply Volterra series to analyze multi-layered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme. To demonstrate the applicability of Volterra series, we analyze a multilayered perceptron trained using Mel filter bank energy features and analyze its first order Volterra kernels.