@inproceedings{162d459617bd4e0ab84a718377cb512f,
title = "Feature extraction and normalization in SVM-based speaker recognition",
abstract = "Feature extraction techniques have the capabilit to improve the performance of pattern recognition systems by providing a more relevant representation of the data. Principal Component Analysis and Independent Component Analysis are two linear feature extraction techniques which seek to find new representations of the multivariate data. We compare the performance of ICA and PCA on a speaker verification task based on a subset of the NIST 2000 speaker recognition evaluation task. The classification is performed using a Support Vector Machine classifier. The SVM is a discriminative classifier which has been successfully applied to several pattern recognition tasks. We find that the ICA representation outperforms the PCA representation on the speaker verification task.",
keywords = "Feature extraction, Speaker recognition, Support vector machines",
author = "Thembisile Mazibuko and Daniel Mashao",
year = "2006",
language = "English",
isbn = "9806560701",
series = "WMSCI 2006 - The 10th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Proc.",
pages = "260--264",
booktitle = "WMSCI 2006 - The 10th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 - Proc.",
note = "10th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2006, Jointly with the 12th International Conference on Information Systems Analysis and Synthesis, ISAS 2006 ; Conference date: 16-07-2006 Through 19-07-2006",
}