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ICASSP
2009
IEEE

Volterra series for analyzing MLP based phoneme posterior estimator

9 years 7 months ago
Volterra series for analyzing MLP based phoneme posterior estimator
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.
Joel Pinto, Garimella S. V. S. Sivaram, Hynek Herm
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICASSP
Authors Joel Pinto, Garimella S. V. S. Sivaram, Hynek Hermansky, Mathew Magimai-Doss
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