Detail publikace

Discriminatively Re-trained i-Vector Extractor For Speaker Recognition

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

Originální název

Discriminatively Re-trained i-Vector Extractor For Speaker Recognition

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this work we revisit discriminative training of the i-vector extractor component in the standard speaker verification (SV) system. The motivation of our research lies in the robustness and stability of this large generative model, which we want to preserve, and focus its power towards any intended SV task. We show that after generative initialization of the i-vector extractor, we can further refine it with discriminative training and obtain i-vectors that lead to better performance on various benchmarks representing different acoustic domains.

Klíčová slova

i-vectors, i-vector extractor, speaker recogni-tion, speaker verification, discriminative training

Autoři

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

Vydáno

12. 5. 2019

Nakladatel

IEEE Signal Processing Society

Místo

Brighton

ISBN

978-1-5386-4658-8

Kniha

Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

Strany od

6031

Strany do

6035

Strany počet

5

URL

https://ieeexplore.ieee.org/document/8682590

BibTex

@inproceedings{BUT160000,
  author="Ondřej {Novotný} and Oldřich {Plchot} and Ondřej {Glembek} and Lukáš {Burget} and Pavel {Matějka}",
  title="Discriminatively Re-trained i-Vector Extractor For Speaker Recognition",
  booktitle="Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)",
  year="2019",
  pages="6031--6035",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8682590",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/document/8682590"
}