PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combined features and kernel design for noise robust phoneme classification using support vector machines
J K Yousafzai, Z Cvetkovic, P Sollich and B Yu
IEEE Transactions on Audio, Speech and Language Processing 2010.


This work proposes methods for combining cepstral and acoustic waveform representations for a front-end of support vector machine (SVM) based speech recognition systems that are robust to additive noise. The key issue of kernel design and noise adaptation for the acoustic waveform representation is addressed first. Cepstral and acoustic waveform representations are then compared on a phoneme classification task. Experiments show that the cepstral features achieve very good performance in low noise conditions, but suffer severe performance degradation already at moderate noise levels. Classification in the acoustic waveform domain, on the other hand, is less accurate in low noise but exhibits a more robust behavior in high noise conditions. A combination of the cepstral and acoustic waveform representations achieves better classification performance than either of the individual representations over the entire range of noise levels tested, down to -18dB SNR.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7685
Deposited By:Jibran Yousafzai
Deposited On:17 March 2011