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Description
  • This paper deals with utilization of maximum likelihood linear regression (MLLR) adaptation transforms for speaker recognition in broadcast news streams. This task is specific particularly for widely varying acoustic conditions, microphones, transmission channels, background noise and short duration of recordings (usually in the range from 5 to 15 seconds). MLLR transforms based features are modeled using support vector machines (SVM). Obtained results are compared with a GMM based system with traditional MFCC features. The paper also deals with inter-session variability compensation techniques suitable for both systems and emphases the importance of feature vector scaling for SVM based system.
  • This paper deals with utilization of maximum likelihood linear regression (MLLR) adaptation transforms for speaker recognition in broadcast news streams. This task is specific particularly for widely varying acoustic conditions, microphones, transmission channels, background noise and short duration of recordings (usually in the range from 5 to 15 seconds). MLLR transforms based features are modeled using support vector machines (SVM). Obtained results are compared with a GMM based system with traditional MFCC features. The paper also deals with inter-session variability compensation techniques suitable for both systems and emphases the importance of feature vector scaling for SVM based system. (en)
Title
  • MLLR Transforms Based Speaker Recognition in Broadcast Streams
  • MLLR Transforms Based Speaker Recognition in Broadcast Streams (en)
skos:prefLabel
  • MLLR Transforms Based Speaker Recognition in Broadcast Streams
  • MLLR Transforms Based Speaker Recognition in Broadcast Streams (en)
skos:notation
  • RIV/46747885:24220/09:#0001399!RIV10-MV0-24220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(VD20072010B16)
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
  • 326569
http://linked.open...ai/riv/idVysledku
  • RIV/46747885:24220/09:#0001399
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • speaker recognition; MLLR, NAP (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F1BFD40E31E3]
http://linked.open...v/mistoKonaniAkce
  • Prague, CZECH REPUBLIC
http://linked.open...i/riv/mistoVydani
  • BERLIN, GERMANY
http://linked.open...i/riv/nazevZdroje
  • Lecture Notes in Artificial Inteligence, LNAI 5641
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
  • Silovský, Jan
  • Červa, Petr
  • Žďánský, Jindřich
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://purl.org/ne...btex#hasPublisher
  • SPRINGER-VERLAG BERLIN
https://schema.org/isbn
  • 978-3-642-03319-3
http://localhost/t...ganizacniJednotka
  • 24220
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