Robustness of Phoneme Classification using Support Vector Machines: A Comparison between PLP and Acoustic Waveform Representations
Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich and Matthew Ager
In: ICASSP 2008, 30 Mar - 4 Apr 2008, Las Vegas.
Robustness of phoneme recognition to additive noise is investigated for PLP and acoustic waveform representations of speech using support vector machines (SVMs) combined via error-correcting code methods.
While recognition in the PLP domain attains superb accuracy on clean
data, it is significantly affected by mismatch between training and testing noise levels. The classification in the high-dimensional acoustic waveform domain, on the other hand, is more robust to additive noise. Moreover, these classifiers perform best when trained on clean data. We also show that the simpler structure of the waveform representation allows one to improve performance using custom-designed kernel functions.