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Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator
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.
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Name
Pinto_Idiap-RR-69-2008.pdf
Access type
openaccess
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525.74 KB
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Adobe PDF
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