Publication detail

Discriminatively Re-trained i-Vector Extractor For Speaker Recognition

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

Original Title

Discriminatively Re-trained i-Vector Extractor For Speaker Recognition

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

12. 5. 2019

Publisher

IEEE Signal Processing Society

Location

Brighton

ISBN

978-1-5386-4658-8

Book

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

Pages from

6031

Pages to

6035

Pages count

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"
}