PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combined PLP-Acoustic Waveform Classification for Robust Phoneme Recognition using Support Vector Machines
Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich and Bin Yu
In: EUSIPCO 2008, Lausanne(2008).


The robustness of phoneme classification using support vector machines (SVMs) to additive white Gaussian noise is investigated in acoustic waveform and PLP domains. Classification in the PLP space gives excellent results at low noise level under matched training and testing conditions, but it is very sensitive to their mismatch. On the other hand, classification in the acoustic waveform domain is inferior at low noise levels, but exhibits a much more robust behaviour, and at high noise levels even with training on clean data significantly outperforms the classification in the PLP space with training under matched conditions. The two classifiers are then combined in a manner which attains the accuracy of PLP at low noise levels and significantly improves its robustness to additive noise.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4051
Deposited By:Peter Sollich
Deposited On:25 February 2008