Publication detail

Speaker Verification Using End-To-End Adversarial Language Adaptation

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

Original Title

Speaker Verification Using End-To-End Adversarial Language Adaptation

Type

conference paper

Language

English

Original Abstract

In this paper we investigate the use of adversarial domain adaptation for addressing the problem of language mismatch between speaker recognition corpora. In the context of speaker verification, adversarial domain adaptation methods aim at minimizing certain divergences between the distribution that the utterance-level features follow (i.e. speaker embeddings) when drawn from source and target domains (i.e. languages), while preserving their capacity in recognizing speakers. Neural architectures for extracting utterancelevel representations enable us to apply adversarial adaptation methods in an end-to-end fashion and train the network jointly with the standard cross-entropy loss. We examine several configurations, such as the use of (pseudo-)labels on the target domain as well as domain labels in the feature extractor, and we demonstrate the effectiveness of our method on the challenging NIST SRE16 and SRE18 benchmarks.

Keywords

Speaker recognition, domain adaptation

Authors

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

Released

12. 5. 2019

Publisher

IEEE Signal Processing Society

Location

Brighton

ISBN

978-1-5386-4658-8

Book

Proceedings of ICASSP 2019

Pages from

6006

Pages to

6010

Pages count

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"
}