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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" }
Documents
rohdin_icassp2019_0006006.pdf