Detail publikačního výsledku

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.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Recently, several end-to-end speaker verification systems based ondeep neural networks (DNNs) have been proposed. These systemshave been proven to be competitive for text-dependent tasks as wellas for text-independent tasks with short utterances. However, fortext-independent tasks with longer utterances, end-to-end systemsare still outperformed by standard i-vector + PLDA systems. In thiswork, we develop an end-to-end speaker verification system that isinitialized to mimic an i-vector + PLDA baseline. The system isthen further trained in an end-to-end manner but regularized so thatit does not deviate too far from the initial system. In this way wemitigate 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.

Anglický abstrakt

Recently, several end-to-end speaker verification systems based ondeep neural networks (DNNs) have been proposed. These systemshave been proven to be competitive for text-dependent tasks as wellas for text-independent tasks with short utterances. However, fortext-independent tasks with longer utterances, end-to-end systemsare still outperformed by standard i-vector + PLDA systems. In thiswork, we develop an end-to-end speaker verification system that isinitialized to mimic an i-vector + PLDA baseline. The system isthen further trained in an end-to-end manner but regularized so thatit does not deviate too far from the initial system. In this way wemitigate 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.

Klíčová slova

Speaker verification, DNN, end-to-end

Klíčová slova v angličtině

Speaker verification, DNN, end-to-end

Autoři

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

Rok RIV

2019

Vydáno

15.04.2018

Nakladatel

IEEE Signal Processing Society

Místo

Calgary

ISBN

978-1-5386-4658-8

Kniha

Proceedings of ICASSP

Strany od

4874

Strany do

4878

Strany počet

5

URL

https://www.fit.vut.cz/research/publication/11724/

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/"
}

Dokumenty

rohdin_icassp2018_0004874