PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discriminative and generative machine learning approaches towards robust phoneme classification
Jibran Yousafzai, Matthew Ager, Zoran Cvetkovic and Peter Sollich
In: Information Theory and Applications 2008, 27 Jan - 1 Feb 2008, University of California San Diego.

Abstract

Robustness of classification of isolated phoneme segments using discriminative and generative classifiers is investigated for the acoustic waveform and PLP speech representations. The two approaches used are support vector machines (SVMs) and mixtures of probabilistic PCA (MPPCA). While recognition in the PLP domain attains superb accuracy on clean data, it is significantly affected by mismatch between training and test noise levels. Classification in the high-dimensional acoustic waveform domain, on the other hand, is more robust in the presence of additive white Gaussian noise. We also show some results on the effects of custom-designed kernel functions for SVM classification in the acoustic waveform domain.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Speech
ID Code:3478
Deposited By:Peter Sollich
Deposited On:11 February 2008