Combined PLP - Acoustic waveform classification for robust phoneme recognition using support vector machines
Jibran Khan Yousafzai, Zoran Cvetković, Peter Sollich, Bin Yu · 2008
The robustness of phoneme classification to additive white Gaussian noise is investigated in acoustic waveform and PLP domains using support vector machines (SVMs). 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. 1.