Publication Details

End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA

ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; PLCHOT, O.; MATĚJKA, P.; BURGET, L. End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA. In Proceedings of ICASSP. Calgary: IEEE Signal Processing Society, 2018. p. 4874-4878. ISBN: 978-1-5386-4658-8.
Czech title
End-to-end DNN rozpoznávání mluvčího inspirované i-vektory a PLDA
Type
conference paper
Language
English
Authors
URL
Keywords

Speaker verification, DNN, end-to-end

Abstract

Recently, several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we develop an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of endto- end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.

Published
2018
Pages
4874–4878
Proceedings
Proceedings of ICASSP
ISBN
978-1-5386-4658-8
Publisher
IEEE Signal Processing Society
Place
Calgary
DOI
UT WoS
000446384605009
EID Scopus
BibTeX
@inproceedings{BUT155046,
  author="Johan Andréas {Rohdin} and Anna {Silnova} and Mireia {Diez Sánchez} and Oldřich {Plchot} and Pavel {Matějka} and Lukáš {Burget}",
  title="End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA",
  booktitle="Proceedings of ICASSP",
  year="2018",
  pages="4874--4878",
  publisher="IEEE Signal Processing Society",
  address="Calgary",
  doi="10.1109/ICASSP.2018.8461958",
  isbn="978-1-5386-4658-8",
  url="https://www.fit.vut.cz/research/publication/11724/"
}
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