Dealing with Diverse Data Variances in Factor Analysis Based Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F13%3A43920612" target="_blank" >RIV/49777513:23520/13:43920612 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-01931-4_14" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-01931-4_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01931-4_14" target="_blank" >10.1007/978-3-319-01931-4_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dealing with Diverse Data Variances in Factor Analysis Based Methods

Popis výsledku v původním jazyce

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 severa

Název v anglickém jazyce

Dealing with Diverse Data Variances in Factor Analysis Based Methods

Popis výsledku anglicky

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 severa

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/TA01030476" target="_blank" >TA01030476: Inteligentní technologie pro zvýšení bezpečnosti letového provozu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Speech and Computer

ISBN

978-3-319-01930-7

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

8

Strana od-do

100-107

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Pilsen, Czech Republic

Datum konání akce

1. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—