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  • The paper deals with the dependence between the speaker identification performance and the amount of test data. Three speaker identification procedures based on hidden Markov models (HMMs) of phonemes are presented here. One, which is quite commonly us ed in the speaker recognition systems based on HMMs, uses the likelihood of the whole utterance for speaker identification. The other two that are proposed in this paper are based on the majority voting rule. The experiments were performed for two diff erent situations: either both training and test data were obtained from the same channel, or they were obtained from different channels. All experiments show that the proposed speaker identification procedure based on the majority voting rule for sequen ces of phonemes allows us to reduce the amount of test data necessary for successful speaker identification.
  • The paper deals with the dependence between the speaker identification performance and the amount of test data. Three speaker identification procedures based on hidden Markov models (HMMs) of phonemes are presented here. One, which is quite commonly us ed in the speaker recognition systems based on HMMs, uses the likelihood of the whole utterance for speaker identification. The other two that are proposed in this paper are based on the majority voting rule. The experiments were performed for two diff erent situations: either both training and test data were obtained from the same channel, or they were obtained from different channels. All experiments show that the proposed speaker identification procedure based on the majority voting rule for sequen ces of phonemes allows us to reduce the amount of test data necessary for successful speaker identification. (en)
Title
  • On the amount of speech data necessary for successful speaker identification
  • On the amount of speech data necessary for successful speaker identification (en)
skos:prefLabel
  • On the amount of speech data necessary for successful speaker identification
  • On the amount of speech data necessary for successful speaker identification (en)
skos:notation
  • RIV/49777513:23520/03:00000154!RIV/2004/GA0/235204/N
http://linked.open.../vavai/riv/strany
  • 3021-3024
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/02/0124), Z(MSM 235200004)
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
  • 619418
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/03:00000154
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • speaker identification;closed set;reduction of amount of test data;HMM based approach (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [692DD31B640C]
http://linked.open...v/mistoKonaniAkce
  • Geneva
http://linked.open...i/riv/mistoVydani
  • Geneva
http://linked.open...i/riv/nazevZdroje
  • EUROSPEECH 2003 PROCEEDINGS
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Padrta, Aleš
  • Radová, Vlasta
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • ISCA
http://localhost/t...ganizacniJednotka
  • 23520
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