About: Dealing with Diverse Data Variances in Factor Analysis Based Methods

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Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007/978-3-319-01931-4_14
Description	Probabilistic Linear Discriminant Analysis (PLDA) and the concept of i-vectors are state-of-the-art methods used in the speaker recognition. They are based on Factor Analysis, in which a data covariance matrix is decomposed in order to find a low dimensional representation of given feature vectors. More precisely, the Factor Analysis based methods seek for directions/subspaces in which the projected (overall/between/within) variance is highest. In order to train models related to individual methods, development speech corpora comprising various acoustic conditions are utilized. The higher are the variations in some of these acoustic conditions, the more will the model tend to reflect them. Strong data variations in some of the development corpora may suppress conditions present in other corpora. This can lead to poor recognition when acoustic variations in test conditions significantly differ. In this paper techniques alleviating such effects are investigated. The idea is to use several background and i-vector models related to different parts of development data so that several i-vectors are extracted, processed and handed over to the PLDA modelling. PLDA model is then used to utilize all the extracted information and provide the verification result. Probabilistic Linear Discriminant Analysis (PLDA) and the concept of i-vectors are state-of-the-art methods used in the speaker recognition. They are based on Factor Analysis, in which a data covariance matrix is decomposed in order to find a low dimensional representation of given feature vectors. More precisely, the Factor Analysis based methods seek for directions/subspaces in which the projected (overall/between/within) variance is highest. In order to train models related to individual methods, development speech corpora comprising various acoustic conditions are utilized. The higher are the variations in some of these acoustic conditions, the more will the model tend to reflect them. Strong data variations in some of the development corpora may suppress conditions present in other corpora. This can lead to poor recognition when acoustic variations in test conditions significantly differ. In this paper techniques alleviating such effects are investigated. The idea is to use several background and i-vector models related to different parts of development data so that several i-vectors are extracted, processed and handed over to the PLDA modelling. PLDA model is then used to utilize all the extracted information and provide the verification result. (en)
Title	Dealing with Diverse Data Variances in Factor Analysis Based Methods Dealing with Diverse Data Variances in Factor Analysis Based Methods (en)
skos:prefLabel	Dealing with Diverse Data Variances in Factor Analysis Based Methods Dealing with Diverse Data Variances in Factor Analysis Based Methods (en)
skos:notation	RIV/49777513:23520/13:43920612!RIV14-TA0-23520___
http://linked.open...avai/predkladatel	Fakulta aplikovaných věd
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(TA01030476)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Machlica, Lukáš
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	68058
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/13:43920612
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	speaker recognition, PLDA, i-vector, factor analysis, decomposition (en)
http://linked.open.../riv/klicoveSlovo	speaker recognition i-vector decomposition factor analysis PLDA
http://linked.open...ontrolniKodProRIV	[8EDE1A210005]
http://linked.open...v/mistoKonaniAkce	Pilsen, Czech Republic
http://linked.open...i/riv/mistoVydani	Cham
http://linked.open...i/riv/nazevZdroje	Speech and Computer
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...vavai/riv/projekt	Intelligent technologies for improving air traffic security
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Machlica, Lukáš
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-09-01 (xsd:date)
issn	0302-9743
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-01931-4_14
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-319-01930-7
http://localhost/t...ganizacniJednotka	23520

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