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