Volterra Series for Analyzing MLP based Phoneme Posterior Probability 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.


Presented at:
Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year:
2009
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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