Detail publikačního výsledku

Speaker Verification Using End-To-End Adversarial Language Adaptation

ROHDIN, J.; STAFYLAKIS, T.; SILNOVA, A.; ZEINALI, H.; BURGET, L.; PLCHOT, O.

Originální název

Speaker Verification Using End-To-End Adversarial Language Adaptation

Anglický název

Speaker Verification Using End-To-End Adversarial Language Adaptation

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

In this paper we investigate the use of adversarial domainadaptation for addressing the problem of language mismatchbetween speaker recognition corpora. In the context ofspeaker verification, adversarial domain adaptation methodsaim at minimizing certain divergences between the distributionthat the utterance-level features follow (i.e. speakerembeddings) when drawn from source and target domains(i.e. languages), while preserving their capacity in recognizingspeakers. Neural architectures for extracting utterancelevelrepresentations enable us to apply adversarial adaptationmethods in an end-to-end fashion and train the networkjointly with the standard cross-entropy loss. We examineseveral configurations, such as the use of (pseudo-)labels onthe target domain as well as domain labels in the feature extractor,and we demonstrate the effectiveness of our methodon the challenging NIST SRE16 and SRE18 benchmarks.

Anglický abstrakt

In this paper we investigate the use of adversarial domainadaptation for addressing the problem of language mismatchbetween speaker recognition corpora. In the context ofspeaker verification, adversarial domain adaptation methodsaim at minimizing certain divergences between the distributionthat the utterance-level features follow (i.e. speakerembeddings) when drawn from source and target domains(i.e. languages), while preserving their capacity in recognizingspeakers. Neural architectures for extracting utterancelevelrepresentations enable us to apply adversarial adaptationmethods in an end-to-end fashion and train the networkjointly with the standard cross-entropy loss. We examineseveral configurations, such as the use of (pseudo-)labels onthe target domain as well as domain labels in the feature extractor,and we demonstrate the effectiveness of our methodon the challenging NIST SRE16 and SRE18 benchmarks.

Klíčová slova

Speaker recognition, domain adaptation

Klíčová slova v angličtině

Speaker recognition, domain adaptation

Autoři

ROHDIN, J.; STAFYLAKIS, T.; SILNOVA, A.; ZEINALI, H.; BURGET, L.; PLCHOT, O.

Rok RIV

2020

Vydáno

12.05.2019

Nakladatel

IEEE Signal Processing Society

Místo

Brighton

ISBN

978-1-5386-4658-8

Kniha

Proceedings of ICASSP 2019

Strany od

6006

Strany do

6010

Strany počet

5

URL

https://ieeexplore.ieee.org/abstract/document/8683616

BibTex

@inproceedings{BUT158086,
  author="Johan Andréas {Rohdin} and Themos {Stafylakis} and Anna {Silnova} and Hossein {Zeinali} and Lukáš {Burget} and Oldřich {Plchot}",
  title="Speaker Verification Using End-To-End Adversarial Language Adaptation",
  booktitle="Proceedings of ICASSP 2019",
  year="2019",
  pages="6006--6010",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8683616",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/abstract/document/8683616"
}

Dokumenty

rohdin_icassp2019_0006006