Detail publikace

Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition

NOVOTNÝ, O. PLCHOT, O. GLEMBEK, O. BURGET, L.

Originální název

Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the large number of parameters of the original generative ivector model. Our aim is to preserve the power of the original generative model, and at the same time focus the model towards extraction of speaker-related information. We show that it is possible to represent a standard generative i-vector extractor by a model with significantly less parameters and obtain similar performance on SV tasks. We can further refine this compact model by discriminative training and obtain i-vectors that lead to better performance on various SV benchmarks representing different acoustic domains.

Klíčová slova

SRE

Autoři

NOVOTNÝ, O.; PLCHOT, O.; GLEMBEK, O.; BURGET, L.

Vydáno

15. 9. 2019

Nakladatel

International Speech Communication Association

Místo

Graz

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2019

Číslo

9

Stát

Francouzská republika

Strany od

4330

Strany do

4334

Strany počet

5

URL

https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1757.pdf

BibTex

@inproceedings{BUT159998,
  author="Ondřej {Novotný} and Oldřich {Plchot} and Ondřej {Glembek} and Lukáš {Burget}",
  title="Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition",
  booktitle="Proceedings of Interspeech",
  year="2019",
  journal="Proceedings of Interspeech",
  volume="2019",
  number="9",
  pages="4330--4334",
  publisher="International Speech Communication Association",
  address="Graz",
  doi="10.21437/Interspeech.2019-1757",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1757.pdf"
}

Dokumenty

novotny_is2019_191757.pdf