Publication detail

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.

Original Title

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

Type

conference paper

Language

English

Original 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.

Keywords

Speaker verification, DNN, end-to-end

Authors

ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; PLCHOT, O.; MATĚJKA, P.; BURGET, L.

Released

15. 4. 2018

Publisher

IEEE Signal Processing Society

Location

Calgary

ISBN

978-1-5386-4658-8

Book

Proceedings of ICASSP

Pages from

4874

Pages to

4878

Pages count

5

URL

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/"
}