PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving the Robustness of Phoneme Classification Using Hybrid Features
Jibran Yousafzai, Zoran Cvetkovic and Peter Sollich
In: ISIT 2010, 13-18 June 2010, Austin, Texas.

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Abstract

This work is concerned with improving the robustness of phoneme classification to additive noise with hybrid features using support vector machines (SVMs). In particular, the cepstral features are combined with local energy features of acoustic waveform segments to form a hybrid representation. The local energy features are taken into account separately in the SVM kernel, and a simple subtraction method allows them to be adapted effectively in noise. This hybrid representation with mean and variance normalization of the cepstral features contributes significantly to the robustness of phoneme classification and narrows the performance gap to the ideal baseline of classifiers trained under matched noise conditions. Further improvements are obtained by extending the multiclass prediction method from standard discrete error-correcting codes to adaptive continuous codes.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
ID Code:7684
Deposited By:Jibran Yousafzai
Deposited On:17 March 2011

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