Speaker Verification and Identification Using Principal Component Analysis Based on Global Eigenvector Matrix

Influence of the Number of Principal Components used to the Automatic Speaker Recognition Accuracy

“…The derivation of the eigenvectors, EVect , is based on the fact that each eigenvector is described by the appropriate eigenvalue, EVal , with respect to the used covariance matrix C [4], as follows…”

“…Recent work indicates that PCA is a prominent technique for dimension reduction. 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.…”

“…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].…”

“…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.…”