Robust speaker identification using ensembles of kernel principal component analysis

Authors:
IL-Ho Yang;Min-Seok Kim;Byung-Min So;Myung-Jae Kim;Ha-Jin Yu
Affiliations:
School of Computer Science, University of Seoul, Seoul, Korea;Advanced Research Institute, LG Electronics Inc., Seoul, Korea;School of Computer Science, University of Seoul, Seoul, Korea;School of Computer Science, University of Seoul, Seoul, Korea;School of Computer Science, University of Seoul, Seoul, Korea
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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Abstract

In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).