Result Details

13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE

MATĚJKA, P.; PLCHOT, O.; GLEMBEK, O.; BURGET, L.; ROHDIN, J.; ZEINALI, H.; MOŠNER, L.; SILNOVA, A.; NOVOTNÝ, O.; DIEZ SÁNCHEZ, M.; ČERNOCKÝ, J. 13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE. COMPUTER SPEECH AND LANGUAGE, 2020, vol. 2020, no. 63, p. 1-15. ISSN: 0885-2308.
Type
journal article
Language
English
Authors
Matějka Pavel, Ing., Ph.D., DCGM (FIT)
Plchot Oldřich, Ing., Ph.D., DCGM (FIT)
Glembek Ondřej, Ing., Ph.D., DCGM (FIT)
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
Rohdin Johan Andréas, M.Sc., Ph.D., FIT (FIT), DCGM (FIT)
Zeinali Hossein, Ph.D.
Mošner Ladislav, Ing., DCGM (FIT)
Silnova Anna, M.Sc., Ph.D., DCGM (FIT)
Novotný Ondřej, Ing., Ph.D., DCGM (FIT)
Diez Sánchez Mireia, M.Sc., Ph.D., DCGM (FIT)
Černocký Jan, prof. Dr. Ing., DCGM (FIT)
Abstract

In this paper, we present a brief history and a "longitudinal study" of all important milestonemodelling techniques used in text independent speaker recognition since Brno University ofTechnology (BUT) first participated in the NIST Speaker Recognition Evaluation (SRE) in2006-GMM MAP, GMM MAP with eigen-channel adaptation, Joint Factor Analysis, i-vectorand DNN embedding (x-vector). To emphasize the historical context, the techniques areevaluated on all NIST SRE sets since 2004 on a time-machine principle, i.e. a system is alwaystrained using all data available up till the year of evaluation. Moreover, as user-contributedaudiovisual content dominates nowadays Internet, we representatively include the SpeakersIn The Wild (SITW) and VOiCES challenge datasets in the evaluation of our systems. Not onlywe present a comparison of the modelling techniques, but we also show the effect of samplingfrequency.

Keywords

Speaker recognition, NIST, Evaluations, GMM, Eigen-channel, compensation, JFA, I-vectors, DNN Embedding, X-vectors

URL
Published
2020
Pages
1–15
Journal
COMPUTER SPEECH AND LANGUAGE, vol. 2020, no. 63, ISSN 0885-2308
DOI
UT WoS
000534481900003
EID Scopus
BibTeX
@article{BUT162674,
  author="Pavel {Matějka} and Oldřich {Plchot} and Ondřej {Glembek} and Lukáš {Burget} and Johan Andréas {Rohdin} and Hossein {Zeinali} and Ladislav {Mošner} and Anna {Silnova} and Ondřej {Novotný} and Mireia {Diez Sánchez} and Jan {Černocký}",
  title="13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE",
  journal="COMPUTER SPEECH AND LANGUAGE",
  year="2020",
  volume="2020",
  number="63",
  pages="1--15",
  doi="10.1016/j.csl.2019.101035",
  issn="0885-2308",
  url="https://www.sciencedirect.com/science/article/pii/S0885230819302797?via%3Dihub"
}
Files
Projects
Improving Robustnes in Automatic Speaker Recognition, GACR, Juniorské granty, GJ17-23870Y, start: 2017-01-01, end: 2019-12-31, completed
Information mining in speech acquired by distant microphones, MV, Bezpečnostní výzkum České republiky 2015-2020, VI20152020025, start: 2015-10-01, end: 2020-09-30, completed
IT4Innovations excellence in science, MŠMT, Národní program udržitelnosti II, LQ1602, start: 2016-01-01, end: 2020-12-31, completed
Moderní metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-20-6460, start: 2020-03-01, end: 2023-02-28, completed
Neural networks for signal processing and speech data mining, TAČR, Program na podporu aplikovaného výzkumu ZÉTA, TJ01000208, start: 2018-01-01, end: 2019-12-31, completed
Neural Representations in multi-modal and multi-lingual modeling, GACR, Grantové projekty exelence v základním výzkumu EXPRO - 2019, GX19-26934X, start: 2019-01-01, end: 2023-12-31, completed
Robust SPEAKER DIariazation systems using Bayesian inferenCE and deep learning methods, EU, Horizon 2020, start: 2017-03-01, end: 2019-02-28, completed
Sequence summarizing neural networks for speaker recognition, EU, Horizon 2020, 5SA15094, start: 2016-07-01, end: 2019-06-30, completed
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