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conference paper
Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing,
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.
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Pinto_ICASSP_2009.pdf
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