Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator
Joel Pinto, Garimella S. V. S. Sivaram, Hynek Heřmanský, Mathew Magimai.-Doss · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2008
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 us-ing Mel filter bank energy features and analyze its first order Volterra kernels. Index Terms — Volterra series, multilayered perceptrons, speech recognition