Publication detail

Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition

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

Original Title

Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition

Type

conference paper

Language

English

Original Abstract

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.

Keywords

SRE

Authors

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

Released

15. 9. 2019

Publisher

International Speech Communication Association

Location

Graz

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Year of study

2019

Number

9

State

French Republic

Pages from

4330

Pages to

4334

Pages count

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"
}

Documents

novotny_is2019_191757.pdf