Publication Details
Speaker Verification Using End-To-End Adversarial Language Adaptation
Stafylakis Themos
Silnova Anna, M.Sc., Ph.D. (DCGM)
Zeinali Hossein, Ph.D. (DCGM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Plchot Oldřich, Ing., Ph.D. (DCGM)
Speaker recognition, domain adaptation
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.
@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"
}