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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A00149485%21RIV10-MSM-21230___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
The usage of LR and ergodic Markov models in voice activity detection and VAD testing platform is presented in this article. These detectors based on HMMs and EHMMs reach better results than traditional energy or cepstral detectors. The testing of suggested algorithms were realized with data recorded in running car and the contribution is evident especially in this very noisy environment. Commonly with the results of experiment the selection of the data and the design of the VAD testing platform is described in this paper. Used speech records consists of isolated digits, different commands, names and were recorded in environment of quiet car without engine, running car or standing car with running engine. The usage of LR and ergodic Markov models in voice activity detection and VAD testing platform is presented in this article. These detectors based on HMMs and EHMMs reach better results than traditional energy or cepstral detectors. The testing of suggested algorithms were realized with data recorded in running car and the contribution is evident especially in this very noisy environment. Commonly with the results of experiment the selection of the data and the design of the VAD testing platform is described in this paper. Used speech records consists of isolated digits, different commands, names and were recorded in environment of quiet car without engine, running car or standing car with running engine.
dcterms:title
HMM and EHMM Based Voice Activity Detectors and Design of Testing Platform for VAD Classification HMM and EHMM Based Voice Activity Detectors and Design of Testing Platform for VAD Classification
skos:prefLabel
HMM and EHMM Based Voice Activity Detectors and Design of Testing Platform for VAD Classification HMM and EHMM Based Voice Activity Detectors and Design of Testing Platform for VAD Classification
skos:notation
RIV/68407700:21230/08:00149485!RIV10-MSM-21230___
n4:aktivita
n13:P n13:Z
n4:aktivity
P(GA102/08/0707), P(GD102/08/H008), Z(MSM6840770014)
n4:dodaniDat
n11:2010
n4:domaciTvurceVysledku
n10:9703861 n10:6738524
n4:druhVysledku
n9:D
n4:duvernostUdaju
n16:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
370338
n4:idVysledku
RIV/68407700:21230/08:00149485
n4:jazykVysledku
n21:eng
n4:klicovaSlova
EHMM; HMM; VAD; ergodic hidden Markov models; voice activity detection
n4:klicoveSlovo
n12:voice%20activity%20detection n12:ergodic%20hidden%20Markov%20models n12:VAD n12:EHMM n12:HMM
n4:kontrolniKodProRIV
[68CDF6EE3504]
n4:mistoKonaniAkce
Žilina
n4:mistoVydani
Žilina
n4:nazevZdroje
Digital Technologies 2008
n4:obor
n19:JA
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n8:GD102%2F08%2FH008 n8:GA102%2F08%2F0707
n4:rokUplatneniVysledku
n11:2008
n4:tvurceVysledku
Pollák, Petr Tatarinov, Jiří
n4:typAkce
n5:WRD
n4:zahajeniAkce
2008-11-20+01:00
n4:zamer
n22:MSM6840770014
s:numberOfPages
4
n20:hasPublisher
Žilinská univerzita v Žiline. Elektrotechnická fakulta
n18:isbn
978-80-8070-953-2
n3:organizacniJednotka
21230