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NOVOTNÝ, O.; MATĚJKA, P.; GLEMBEK, O.; PLCHOT, O.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.
Original Title
DNN-based SRE Systems in Multi-Language Conditions
English Title
Type
Research report
Original Abstract
This work studies the usage of the (currently state-of-the-art) Deep NeuralNetworks (DNN) i-vector/PLDA-based speaker recognition systems inmulti-language (especially non-English) conditions. On the ``Language Pack''of the PRISM set, we evaluate the systems' performance using NIST's standardmetrics. We study the use of multi-lingual DNN in place of the originalEnglish DNN on these multi-language conditions. We show that not only the gainfrom using DNNs vanishes, but also the DNN-based systems tend to producede-calibrated scores under the studied conditions. This work gives suggestionsfor directions of future research rather than any particular solutions.
English abstract
Keywords
speaker recognition, multilinguality, DNN
Key words in English
Authors
Released
25.07.2016
Publisher
Faculty of Information Technology BUT
Location
Brno
Pages count
5
URL
http://www.fit.vutbr.cz/research/groups/speech/publi/2016/dnn-based-sre_TECH_REP_v0.pdf
BibTex
@techreport{BUT168427, author="Ondřej {Novotný} and Pavel {Matějka} and Ondřej {Glembek} and Oldřich {Plchot} and František {Grézl} and Lukáš {Burget} and Jan {Černocký}", title="DNN-based SRE Systems in Multi-Language Conditions", year="2016", publisher="Faculty of Information Technology BUT", address="Brno", pages="5", url="http://www.fit.vutbr.cz/research/groups/speech/publi/2016/dnn-based-sre_TECH_REP_v0.pdf" }