On Complementarity of State-of-the-art Speaker Recognition Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43916022" target="_blank" >RIV/49777513:23520/12:43916022 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

On Complementarity of State-of-the-art Speaker Recognition Systems

Popis výsledku v původním jazyce

In this paper recent methods used in the task of Speaker Recognition (SR) are reviewed and their complementarity is analysed. At first, methods based on Supervectors (SVs) related to Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs) usedas a discriminative model are described along with the Nuisance Attribute Projection (NAP). NAP was proposed to suppress undesirable influences of high channel variabilities between several sessions of a speaker. Next, recent methods focusing on the extraction of so called i-vectors (low dimensional representations of GMM based SVs) are discussed. The space in which i-vectors lie is denoted the Total Variability Space (TVS) since it contains both between-speaker and session/channel variabilities. Once i-vectors have been extracted a Probabilistic Linear Discriminant Analysis (PLDA) model is trained in the TVS. In the training phase of PLDA the TVS is decomposed to a channel and a speaker subspace, hence each i-vector is supposed to be c

Název v anglickém jazyce

On Complementarity of State-of-the-art Speaker Recognition Systems

Popis výsledku anglicky

In this paper recent methods used in the task of Speaker Recognition (SR) are reviewed and their complementarity is analysed. At first, methods based on Supervectors (SVs) related to Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs) usedas a discriminative model are described along with the Nuisance Attribute Projection (NAP). NAP was proposed to suppress undesirable influences of high channel variabilities between several sessions of a speaker. Next, recent methods focusing on the extraction of so called i-vectors (low dimensional representations of GMM based SVs) are discussed. The space in which i-vectors lie is denoted the Total Variability Space (TVS) since it contains both between-speaker and session/channel variabilities. Once i-vectors have been extracted a Probabilistic Linear Discriminant Analysis (PLDA) model is trained in the TVS. In the training phase of PLDA the TVS is decomposed to a channel and a speaker subspace, hence each i-vector is supposed to be c

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE International Symposium on Signal Processing and Information Technology

ISBN

978-1-4673-5604-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers ( IEEE )

Místo vydání

Neuveden

Místo konání akce

Vietnam, Ho Chi Minh City

Datum konání akce

12. 12. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—