Towards robust phoneme classification with hybrid features

Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich
2010 2010 IEEE International Symposium on Information Theory  
In this paper, we investigate the robustness of phoneme classification to additive noise with hybrid features using support vector machines (SVMs). In particular, the cepstral features are combined with short term energy features of acoustic waveform segments to form a hybrid representation. The energy features are then taken into account separately in the SVM kernel, and a simple subtraction method allows them to be adapted effectively in noise. This hybrid representation contributes
more » ... ntributes significantly to the robustness of phoneme classification and narrows the performance gap to the ideal baseline of classifiers trained under matched noise conditions.
doi:10.1109/isit.2010.5513345 dblp:conf/isit/YousafzaiCS10 fatcat:sfazjzsgxrcuxidam4bbsmc4yi