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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F14%3APU111948%21RIV15-MSM-26230___
rdf:type
n10:Vysledek skos:Concept
dcterms:description
In this paper, we have shown a technique of within-class correction for Linear Discriminant Analysis estimation. We have shown that when correct dataset clustering is used, adapting the within-class covariance of LDA by low-rank between-dataset covariance matrix can lead to significant improvement of the system, namely up to 70% in the Domain Adaptation Task, and 17.5% and 36% relative in the RATS unmatched and semi-matched tasks, respectively. The dataset clustering problem gave us an interesting direction for future research. In this paper, we have shown a technique of within-class correction for Linear Discriminant Analysis estimation. We have shown that when correct dataset clustering is used, adapting the within-class covariance of LDA by low-rank between-dataset covariance matrix can lead to significant improvement of the system, namely up to 70% in the Domain Adaptation Task, and 17.5% and 36% relative in the RATS unmatched and semi-matched tasks, respectively. The dataset clustering problem gave us an interesting direction for future research.
dcterms:title
Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms
skos:prefLabel
Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms
skos:notation
RIV/00216305:26230/14:PU111948!RIV15-MSM-26230___
n3:aktivita
n17:P
n3:aktivity
P(ED1.1.00/02.0070), P(TA01011328)
n3:dodaniDat
n8:2015
n3:domaciTvurceVysledku
n9:7822995 n9:2912988 n9:3678539 n9:4922514
n3:druhVysledku
n20:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
12155
n3:idVysledku
RIV/00216305:26230/14:PU111948
n3:jazykVysledku
n18:eng
n3:klicovaSlova
speaker recognition, i-vectors, source normalization, LDA, inter-dataset variability compensation
n3:klicoveSlovo
n7:inter-dataset%20variability%20compensation n7:speaker%20recognition n7:i-vectors n7:LDA n7:source%20normalization
n3:kontrolniKodProRIV
[A424233CAF39]
n3:mistoKonaniAkce
Florencie
n3:mistoVydani
Florencie
n3:nazevZdroje
Proceedings of ICASSP 2014
n3:obor
n16:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
7
n3:projekt
n11:ED1.1.00%2F02.0070 n11:TA01011328
n3:rokUplatneniVysledku
n8:2014
n3:tvurceVysledku
Burget, Lukáš Ma, Jeff Glembek, Ondřej Zhang, Bing Matsoukas, Spyros Matějka, Pavel Plchot, Oldřich
n3:typAkce
n4:WRD
n3:zahajeniAkce
2014-05-04+02:00
s:numberOfPages
5
n13:hasPublisher
IEEE Signal Processing Society
n15:isbn
978-1-4799-2892-7
n19:organizacniJednotka
26230