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Lukáš Machlica and Vlasta Radová : On Behaviour of PLDA Models in the Task of Speaker Recognition . Lecture Notes in Artificial Intelligence, vol. 8082, p. 352-359, Springer, Heidelberg, 2013.

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Nowadays, Factor analysis based techniques become part of state-of-the-art Speaker Recognition (SR) systems. These are the Joint Factor Analysis, its modified version called the concept of i-vectors, and the Probabilistic Linear Discriminant Analysis (PLDA). PLDA, as a generative statistical model, is usually used as the back end of a SR system, e.g. once i-vectors have been extracted, a PLDA model is used in the i-vector space to provide a verification score of two given i-vectors. In order to train the system huge amount of development data are utilized. In this paper the behaviour of the PLDA model is investigated. It is shown how does the amount of development data influence the system's performance. PLDA has several parameters to be tuned, i.e. dimensions of latent variables/subspaces, which represent the speaker and the channel variabilities. These will be examined too.

Detail of publication

Title: On Behaviour of PLDA Models in the Task of Speaker Recognition
Author: Lukáš Machlica ; Vlasta Radová
Language: English
Date of publication: 1 Sep 2013
Year: 2013
Type of publication: Papers in proceedings of reviewed conferences
Series: Lecture Notes in Artificial Intelligence
Číslo vydání: 8082
Page: 352 - 359
DOI: 10.1007/978-3-642-40585-3_45
ISBN: 978-3-319-01930-7
ISSN: 0302-9743
Publisher: Springer
Address: Heidelberg
Date: 1 Sep 2013 - 5 Sep 2013
/ 2015-09-18 13:38:58 /


PLDA, i-vectors, robustness, speaker recognition


 author = {Luk\'{a}\v{s} Machlica and Vlasta Radov\'{a}},
 title = {On Behaviour of PLDA Models in the Task of Speaker Recognition},
 year = {2013},
 publisher = {Springer},
 address = {Heidelberg},
 volume = {8082},
 pages = {352-359},
 series = {Lecture Notes in Artificial Intelligence},
 ISBN = {978-3-319-01930-7},
 ISSN = {0302-9743},
 doi = {10.1007/978-3-642-40585-3_45},
 url = {},