On Behaviour of PLDA Models in the Task of Speaker Recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F13%3A43920609" target="_blank" >RIV/49777513:23520/13:43920609 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_45" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_45</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-40585-3_45" target="_blank" >10.1007/978-3-642-40585-3_45</a>

Result language
angličtina
Original language name
On Behaviour of PLDA Models in the Task of Speaker Recognition
Original language 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.
Czech name
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Czech description
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Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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