Analysis of the Influence of Speech Corpora in the PLDA Verification in the Task of Speaker Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43916020" target="_blank" >RIV/49777513:23520/12:43916020 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/content/pdf/10.1007%2F978-3-642-32790-2_56" target="_blank" >http://link.springer.com/content/pdf/10.1007%2F978-3-642-32790-2_56</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_56" target="_blank" >10.1007/978-3-642-32790-2_56</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of the Influence of Speech Corpora in the PLDA Verification in the Task of Speaker Recognition

Popis výsledku v původním jazyce

In the paper recent methods used in the task of speaker recognition are presented. At first, the extraction of so called i-vectors from GMM based supervectors is discussed. These i-vectors are of low dimension and lie in a subspace denoted as Total Variability Space (TVS). The focus of the paper is put on Probabilistic Linear Discriminant Analysis (PLDA), which is used as a generative model in the TVS. The influence of development data is analyzed utilizing distinct speech corpora. It is shown that it is preferable to cluster available speech corpora to classes, train one PLDA model for each class and fuse the results at the end. Experiments are presented on NIST Speaker Recognition Evaluation (SRE) 2008 and NIST SRE 2010.

Název v anglickém jazyce

Analysis of the Influence of Speech Corpora in the PLDA Verification in the Task of Speaker Recognition

Popis výsledku anglicky

In the paper recent methods used in the task of speaker recognition are presented. At first, the extraction of so called i-vectors from GMM based supervectors is discussed. These i-vectors are of low dimension and lie in a subspace denoted as Total Variability Space (TVS). The focus of the paper is put on Probabilistic Linear Discriminant Analysis (PLDA), which is used as a generative model in the TVS. The influence of development data is analyzed utilizing distinct speech corpora. It is shown that it is preferable to cluster available speech corpora to classes, train one PLDA model for each class and fuse the results at the end. Experiments are presented on NIST Speaker Recognition Evaluation (SRE) 2008 and NIST SRE 2010.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

7499

Číslo periodika v rámci svazku

neuveden

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

464-471

Kód UT WoS článku

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EID výsledku v databázi Scopus

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