Název:	On Comparison of XGBoost and Convolutional Neural Networks for Glottal Closure Instant Detection
Autoři:	Vraštil, Michal Matoušek, Jindřich
Citace zdrojového dokumentu:	VRAŠTIL, M. MATOUŠEK, J. On Comparison of XGBoost and Convolutional Neural Networks for Glottal Closure Instant Detection. In Text, Speech, and Dialogue 24th International Conference, TSD 2021, Olomouc, Czech Republic, September 6–9, 2021, Proceedings. Cham: Springer International Publishing, 2021. s. 448-456. ISBN: 978-3-030-83526-2 , ISSN: 0302-9743
Datum vydání:	2021
Nakladatel:	Springer International Publishing
Typ dokumentu:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85115205309 http://hdl.handle.net/11025/47245
ISBN:	978-3-030-83526-2
ISSN:	0302-9743
Klíčová slova v dalším jazyce:	Glottal closure instant (GCI);Pitch mark;Detection;Classification;Extreme gradient boosting;Convolutional neural network
Abstrakt v dalším jazyce:	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.
Práva:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © Springer
Vyskytuje se v kolekcích:	Konferenční příspěvky / Conference Papers (KKY) OBD

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