Speaker Verification with Application-Aware Beamforming
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134976" target="_blank" >RIV/00216305:26230/19:PU134976 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12152/" target="_blank" >https://www.fit.vut.cz/research/publication/12152/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Speaker Verification with Application-Aware Beamforming
Original language description
Multichannel speech processing applications usually employ beamformers as means of speech enhancement through spatial filtering. Beamformers with learnable parameters require training to minimize a loss function that is not necessarily correlated with the final objective. In this paper, we present a framework employing recent neural network based generalized eigenvalue beamformer and application-specific model that allows for optimization of beamformer w.r.t. target application. In our case, the application is speaker verification which utilizes a speaker embedding (x-vector) extractor that conveniently comes with desired loss. We show that application-specific training of the beamformer brings performance improvements over a system trained in the standard way. We perform our analysis on the recently introduced VOiCES corpus which contains multichannel data and allows us to modify the evaluation trials such that enrollment recordings remain single-channel and test utterances are multichannel.
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
Result was created during the realization of more than one project. More information in the Projects tab.
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 ASRU 2019
ISBN
978-1-7281-0306-8
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
411-418
Publisher name
IEEE Signal Processing Society
Place of publication
Sentosa, Singapore
Event location
Automatic Speech Recognition and Understanding W
Event date
Nov 13, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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