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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. (en)
Title
  • On Behaviour of PLDA Models in the Task of Speaker Recognition
  • On Behaviour of PLDA Models in the Task of Speaker Recognition (en)
skos:prefLabel
  • On Behaviour of PLDA Models in the Task of Speaker Recognition
  • On Behaviour of PLDA Models in the Task of Speaker Recognition (en)
skos:notation
  • RIV/49777513:23520/13:43920609!RIV14-GA0-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GBP103/12/G084)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 93655
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/13:43920609
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • PLDA, i-vectors, robustness, speaker recognition (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [5B8034EB1493]
http://linked.open...v/mistoKonaniAkce
  • Pilsen, Czech Republic
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Text, Speech and Dialogue
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Radová, Vlasta
  • Machlica, Lukáš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-40585-3_45
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-40584-6
http://localhost/t...ganizacniJednotka
  • 23520
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