Custom-Designed SVM Kernels for Improved Robustness of Phoneme Classification
Jibran Yousafzai, Zoran Cvetkovic and Peter Sollich
The robustness of phoneme classiﬁcation to additive white Gaussian noise in the acoustic waveform domain is investigated using support vector machines. We focus on the problem of designing kernels which are tuned to the physical properties of speech. For comparison, results are reported for the PLP representation of speech using standard kernels. We show that major improvements can be achieved by incorporating the properties of speech into kernels. Furthermore, the high-dimensional acoustic waveforms exhibit more robust behavior to additive noise. Finally, we investigate a combination of the PLP and acoustic waveform representations which attains better classiﬁcation than either of the individual representations over a range of noise levels.