Analysis of BUT-PT Submission for NIST LRE 2017

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130760" target="_blank" >RIV/00216305:26230/18:PU130760 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fit.vutbr.cz/research/groups/speech/publi/2018/plchot_odyssey2018_69.pdf" target="_blank" >http://www.fit.vutbr.cz/research/groups/speech/publi/2018/plchot_odyssey2018_69.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of BUT-PT Submission for NIST LRE 2017

Popis výsledku v původním jazyce

In this paper, we summarize our efforts in the NIST Language Recognition Evaluations (LRE) 2017 which resulted in systems providing very competitive and state-of-the-art performance. We provide both the descriptions and the analysis of the systems that we included in our submission. We explain our partitioning of the datasets that we were provided by NIST for training and development, and we follow by describing the features, DNN models and classifiers that were used to produce the final systems. After covering the architecture of our submission, we concentrate on post-evaluation analysis. We compare different DNN Bottle-Neck features, i-vector systems of different sizes and architectures, different classifiers and we present experimental results with data augmentation and with improved architecture of the system based on DNN embeddings. We present the performance of the systems in the Fixed condition (where participants are required to use only predefined data sets) and in addition to official NIST LRE17 evaluation set, we also provide results on our internal development set which can serve as a baseline for other researchers, since all training data are fixed and provided by NIST.

Název v anglickém jazyce

Analysis of BUT-PT Submission for NIST LRE 2017

Popis výsledku anglicky

In this paper, we summarize our efforts in the NIST Language Recognition Evaluations (LRE) 2017 which resulted in systems providing very competitive and state-of-the-art performance. We provide both the descriptions and the analysis of the systems that we included in our submission. We explain our partitioning of the datasets that we were provided by NIST for training and development, and we follow by describing the features, DNN models and classifiers that were used to produce the final systems. After covering the architecture of our submission, we concentrate on post-evaluation analysis. We compare different DNN Bottle-Neck features, i-vector systems of different sizes and architectures, different classifiers and we present experimental results with data augmentation and with improved architecture of the system based on DNN embeddings. We present the performance of the systems in the Fixed condition (where participants are required to use only predefined data sets) and in addition to official NIST LRE17 evaluation set, we also provide results on our internal development set which can serve as a baseline for other researchers, since all training data are fixed and provided by NIST.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of Odyssey 2018 The Speaker and Language Recognition Workshop

ISBN

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ISSN

2312-2846

e-ISSN

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Počet stran výsledku

7

Strana od-do

47-53

Název nakladatele

International Speech Communication Association

Místo vydání

Les Sables d'Olonne

Místo konání akce

Les Sables d'Olonne, France

Datum konání akce

26. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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