Speaker Verification Using End-To-End Adversarial Language Adaptation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU132986" target="_blank" >RIV/00216305:26230/19:PU132986 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/8683616" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8683616</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Speaker Verification Using End-To-End Adversarial Language Adaptation
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GJ17-23870Y" target="_blank" >GJ17-23870Y: Improving Robustnes in Automatic Speaker Recognition</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of ICASSP 2019
ISBN
978-1-5386-4658-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
6006-6010
Publisher name
IEEE Signal Processing Society
Place of publication
Brighton
Event location
Brighton
Event date
May 12, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000482554006047