REPORT Pinto_Idiap-RR-69-2008/IDIAP Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator Pinto, Joel Praveen Sivaram, G. S. V. S. Hermansky, Hynek Magimai-Doss, Mathew EXTERNAL http://publications.idiap.ch/attachments/reports/2008/Pinto_Idiap-RR-69-2008.pdf PUBLIC Idiap-RR-69-2008 2008 Idiap October 2008 We present a framework to apply Volterra series to analyze multilayered 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.