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NOVOTNÝ, O.; MATĚJKA, P.; GLEMBEK, O.; PLCHOT, O.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.
Originální název
DNN-based SRE Systems in Multi-Language Conditions
Anglický název
Druh
Výzkumná zpráva
Originální abstrakt
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.
Anglický abstrakt
Klíčová slova
speaker recognition, multilinguality, DNN
Klíčová slova v angličtině
Autoři
Vydáno
25.07.2016
Nakladatel
Faculty of Information Technology BUT
Místo
Brno
Strany počet
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" }