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Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator
Type of publication: Conference paper
Citation: Pinto_ICASSP_2009
Booktitle: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2009
Crossref: Pinto_Idiap-RR-69-2008:
Abstract: 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.
Projects Idiap
Authors Pinto, Joel Praveen
Sivaram, G. S. V. S.
Hermansky, Hynek
Magimai.-Doss, Mathew
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  • Pinto_ICASSP_2009.pdf