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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F13%3A43920609%21RIV14-GA0-23520___
rdf:type
n11:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_45
dcterms:description
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. 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.
dcterms:title
On Behaviour of PLDA Models in the Task of Speaker Recognition On Behaviour of PLDA Models in the Task of Speaker Recognition
skos:prefLabel
On Behaviour of PLDA Models in the Task of Speaker Recognition On Behaviour of PLDA Models in the Task of Speaker Recognition
skos:notation
RIV/49777513:23520/13:43920609!RIV14-GA0-23520___
n11:predkladatel
n12:orjk%3A23520
n3:aktivita
n20:P
n3:aktivity
P(GBP103/12/G084)
n3:dodaniDat
n18:2014
n3:domaciTvurceVysledku
n10:3548481 n10:8612889
n3:druhVysledku
n14:D
n3:duvernostUdaju
n24:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
93655
n3:idVysledku
RIV/49777513:23520/13:43920609
n3:jazykVysledku
n13:eng
n3:klicovaSlova
PLDA, i-vectors, robustness, speaker recognition
n3:klicoveSlovo
n4:robustness n4:PLDA n4:i-vectors n4:speaker%20recognition
n3:kontrolniKodProRIV
[5B8034EB1493]
n3:mistoKonaniAkce
Pilsen, Czech Republic
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Text, Speech and Dialogue
n3:obor
n16:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n23:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n18:2013
n3:tvurceVysledku
Machlica, Lukáš Radová, Vlasta
n3:typAkce
n22:WRD
n3:zahajeniAkce
2013-09-01+02:00
s:issn
0302-9743
s:numberOfPages
8
n8:doi
10.1007/978-3-642-40585-3_45
n17:hasPublisher
Springer-Verlag
n19:isbn
978-3-642-40584-6
n9:organizacniJednotka
23520