All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

On the use of X-vectors for Robust Speaker Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130767" target="_blank" >RIV/00216305:26230/18:PU130767 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11787" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11787</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Odyssey.2018-24" target="_blank" >10.21437/Odyssey.2018-24</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On the use of X-vectors for Robust Speaker Recognition

  • Original language description

    Text-independent speaker verification (SV) is currently in the process of embracing DNN modeling in every stage of SV system. Slowly, the DNN-based approaches such as end-to-end modelling and systems based on DNN embeddings start to be competitive even in challenging and diverse channel conditions of recent NIST SREs. Domain adaptation and the need for a large amount of training data are still a challenge for current discriminative systems and (unlike with generative models), we see significant gains from data augmentation, simulation and other techniques designed to overcome lack of training data. We present an analysis of a SV system based on DNN embeddings (x-vectors) and focus on robustness across diverse data domains such as standard telephone and microphone conversations, both in clean, noisy and reverberant environments. We also evaluate the system on challenging far-field data created by re-transmitting a subset of NIST SRE 2008 and 2010 microphone interviews. We compare our results with the stateof- the-art i-vector system. In general, we were able to achieve better performance with the DNN-based systems, but most importantly, we have confirmed the robustness of such systems across multiple data domains.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of Odyssey 2018

  • ISBN

  • ISSN

    2312-2846

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    168-175

  • Publisher name

    International Speech Communication Association

  • Place of publication

    Les Sables d´Olonne

  • Event location

    Les Sables d'Olonne, France

  • Event date

    Jun 26, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article