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On Behaviour of PLDA Models in the Task of Speaker Recognition

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8082)

Abstract

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.

Keywords

  • PDLA
  • i-vectors
  • robustness
  • speaker recognition

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Machlica, L., Radová, V. (2013). On Behaviour of PLDA Models in the Task of Speaker Recognition. In: Habernal, I., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2013. Lecture Notes in Computer Science(), vol 8082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40585-3_45

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  • DOI: https://doi.org/10.1007/978-3-642-40585-3_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40584-6

  • Online ISBN: 978-3-642-40585-3

  • eBook Packages: Computer ScienceComputer Science (R0)