Conference paper

NOVOTNÝ Ondřej, MATĚJKA Pavel, GLEMBEK Ondřej, PLCHOT Oldřich, GRÉZL František, BURGET Lukáš and ČERNOCKÝ Jan. Analysis of the DNN-Based SRE Systems in Multi-language Conditions. In: Proceedings of SLT 2016. San Diego: IEEE Signal Processing Society, 2016, pp. 199-204. ISBN 978-1-5090-4903-5. Available from:
Publication language:english
Original title:Analysis of the DNN-Based SRE Systems in Multi-language Conditions
Title (cs):Analýza systémů pro rozpoznávání mluvčího založených na DNN v multi-lingválních podmínkách
Proceedings:Proceedings of SLT 2016
Conference:2016 IEEE Workshop on Spoken Language Technology
Place:San Diego, US
Publisher:IEEE Signal Processing Society
DNN, Multi-Language, Speaker Recognition
In this work, we have studied the behavior of the DNN techniques in SRE i-vector/PLDA systems, currently considered to be state-ofthe- art, as evaluated on the most common NIST SRE English test sets, such as the NIST SRE 2010, condition 5.
This paper analyzes the behavior of our state-of-the-art Deep Neural Network/i-vector/PLDA-based speaker recognition systems in multi-language conditions. On the "Language Pack" of the PRISM set, we evaluate the systems performance using the NISTs standard metrics. We show that not only the gain from using DNNs vanishes, nor using dedicated DNNs for target conditions helps, but also the DNN-based systems tend to produce de-calibrated scores under the studied conditions. This work gives suggestions for directions of future research rather than any particular solutions to these issues.
   author = {Ond{\v{r}}ej Novotn{\'{y}} and Pavel Mat{\v{e}}jka
	and Ond{\v{r}}ej Glembek and Old{\v{r}}ich Plchot
	and Franti{\v{s}}ek Gr{\'{e}}zl and
	Luk{\'{a}}{\v{s}} Burget and Jan
   title = {Analysis of the DNN-Based SRE Systems in
	Multi-language Conditions},
   pages = {199--204},
   booktitle = {Proceedings of SLT 2016},
   year = 2016,
   location = {San Diego, US},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-5090-4903-5},
   doi = {10.1109/slt.2016.7846265},
   language = {english},
   url = {}

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