2010
DOI: 10.1007/978-3-642-13769-3_34
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
0
0

Year Published

2012
2012
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(4 citation statements)
references
References 10 publications
0
0
0
Order By: Relevance
“…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…”
Section: From Mfcc To Principal Componentsmentioning
confidence: 99%
See 3 more Smart Citations
“…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…”
Section: From Mfcc To Principal Componentsmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations