On Comparison of XGBoost and Convolutional Neural Networks for Glottal Closure Instant Detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962410" target="_blank" >RIV/49777513:23520/21:43962410 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-83527-9_38" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-83527-9_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-83527-9_38" target="_blank" >10.1007/978-3-030-83527-9_38</a>

Result language
angličtina
Original language name
On Comparison of XGBoost and Convolutional Neural Networks for Glottal Closure Instant Detection
Original language description
In this paper, we progress further in the development of an automatic GCI detection model. In previous papers, we compared XGBoost with other supervised learning models just as with a deep one-dimensional convolutional neural network. Here we aimed to compare a deep one-dimensional convolutional neural network, more precisely the InceptionV3 model, with XGBoost and context-aware XGBoost models trained on the same size datasets. Afterward, we wanted to reveal the influence of dataset consistency and size on the XGBoost performance. All newly created models are compared while tested on our custom test dataset. On the publicly available databases, the XGBoost and context-aware XGBoost with the context of length 7 shows similar and better performance than the InceptionV3 model. Also, the consistency of the training dataset shows significant performance improvement in comparison to the older models.
Czech name
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Czech description
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Project
<a href="/en/project/GA19-19324S" target="_blank" >GA19-19324S: Fully Trainable Deep Neural Network Based Czech Text-to-Speech Synthesis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Text, Speech, and Dialogue 24th International Conference, TSD 2021, Olomouc, Czech Republic, September 6–9, 2021, Proceedings
ISBN
978-3-030-83526-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
9
Pages from-to
448-456
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Olomouc, Czech Republic
Event date
Sep 6, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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