GMM Based on Local Fuzzy PCA for Speaker Identification

Lee, JongJoo; Rheem, JaeYeol; Lee, Ki Yong

doi:10.1007/978-3-540-45080-1_141

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2012

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“…Conventional methods for PCA based on the full data covariance matrix require a large amount of training data [4]. In order to reduce the complexity of these methods where the eigenvector matrix of each speaker is calculated, methods for PCA that are performed on all the training data [4,5] (and this paper), or on locally clustered data [6,7] are introduced. In the domain of speaker recognition, PCA is often applied in Gaussian Mixture Models (GMMs) [4,7].…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the complexity of these methods where the eigenvector matrix of each speaker is calculated, methods for PCA that are performed on all the training data [4,5] (and this paper), or on locally clustered data [6,7] are introduced. In the domain of speaker recognition, PCA is often applied in Gaussian Mixture Models (GMMs) [4,7]. In the next section, we introduce the reference environment and the modus of speaker modeling.…”

Section: Introductionmentioning

confidence: 99%

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