Publication detail

DNN-based SRE Systems in Multi-Language Conditions

NOVOTNÝ, O. MATĚJKA, P. GLEMBEK, O. PLCHOT, O. GRÉZL, F. BURGET, L. ČERNOCKÝ, J.

Original Title

DNN-based SRE Systems in Multi-Language Conditions

Type

report

Language

English

Original Abstract

This work studies the usage of the (currently state-of-the-art) Deep Neural Networks (DNN) i-vector/PLDA-based speaker recognition systems in multi-language (especially non-English) conditions. On the ``Language Pack'' of the PRISM set, we evaluate the systems' performance using NIST's standard metrics. We study the use of multi-lingual DNN in place of the original English DNN on these multi-language conditions. We show that not only the gain from using DNNs vanishes, but also the DNN-based systems tend to produce de-calibrated scores under the studied conditions. This work gives suggestions for directions of future research rather than any particular solutions.

Keywords

speaker recognition, multilinguality, DNN

Authors

NOVOTNÝ, O.; MATĚJKA, P.; GLEMBEK, O.; PLCHOT, O.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.

Released

25. 7. 2016

Publisher

Faculty of Information Technology BUT

Location

Brno

Pages count

5

URL

http://www.fit.vutbr.cz/research/groups/speech/publi/2016/dnn-based-sre_TECH_REP_v0.pdf

BibTex

@techreport{BUT168427,
  author="Ondřej {Novotný} and Pavel {Matějka} and Ondřej {Glembek} and Oldřich {Plchot} and František {Grézl} and Lukáš {Burget} and Jan {Černocký}",
  title="DNN-based SRE Systems in Multi-Language Conditions",
  year="2016",
  publisher="Faculty of Information Technology BUT",
  address="Brno",
  pages="5",
  url="http://www.fit.vutbr.cz/research/groups/speech/publi/2016/dnn-based-sre_TECH_REP_v0.pdf"
}