NDLI: A global covariance matrix based principal component analysis for speaker identification

Content Provider	IEEE Xplore Digital Library
Author	Chagnwoo Seo Jun Youn Youngeun Kim Keunho Sim Jaekwan Ko Meihua Zhao Jongkeun Kim Heeae Ko Eunyoung Kim Younghwan Lim
Copyright Year	2009
Description	Author affiliation: The Global School of Media, Soongsil University 511 Sangdo-Dong, Dongjak-Gu, Seoul, 156743, Korea (Chagnwoo Seo; Jun Youn; Youngeun Kim; Keunho Sim; Jaekwan Ko; Meihua Zhao; Jongkeun Kim; Heeae Ko; Eunyoung Kim; Younghwan Lim)
Abstract	Principal component analysis (PCA) is an efficient feature extraction method which reduces the dimensions of the feature vectors and removes the correlation among them, with little loss of information, by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when calculate the full covariance matrix of each speaker. This paper proposes an efficient global covariance matrix-based PCA for speaker identification. The proposed method uses training data from all speakers to calculate the global covariance matrix and then uses this matrix to find the eigenvalue matrix and the eigenvector matrix to perform PCA. During training and testing, our method uses PCA coefficients that are based on global covariance instead of the PCA coefficients of each speaker. Compared to the conventional PCA and the Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity.
Starting Page	245
Ending Page	248
File Size	264348
Page Count	4
File Format	PDF
ISBN	9781424447848
DOI	10.1109/APCC.2009.5375644
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Principal component analysis Training data Functional analysis Information analysis Feature extraction Eigenvalues and eigenfunctions Testing Books global covariance speaker identification principal component analysis Gaussian mixture model
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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