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dc.contributor.authorMatoušek, Jindřich
dc.contributor.authorTihelka, Daniel
dc.date.accessioned2020-03-16T11:00:21Z
dc.date.available2020-03-16T11:00:21Z
dc.date.issued2019
dc.identifier.citationMATOUŠEK, J., TIHELKA, D. Using extreme gradient boosting to detect glottal closure instatnts in speech singal. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019). New York: IEEE, 2019. s. 6515-6519. ISBN 978-1-4799-8131-1 , ISSN 1520-6149.en
dc.identifier.isbn978-1-4799-8131-1
dc.identifier.issn1520-6149
dc.identifier.uri2-s2.0-85069464575
dc.identifier.urihttp://hdl.handle.net/11025/36660
dc.description.abstractIn this paper, we continue to investigate the use of classifiers for the automatic detection of glottal closure instants (GCIs) from the speech signal. We focus on extreme gradient boosting (XGB), a fast and powerful implementation of a gradient boosting algorithm. We show that XGB outperforms other classifiers, achieving GCI detection accuracy F 1 = 98.55% and AUC = 99.90%. The proposed XGB model is also shown to outperform other existing GCI detection algorithms on publicly available databases. Despite using much less training data, the performance of XGB is comparable to a deep convolutional neural network based approach, especially when it is tested on voices that were not included in the training data.en
dc.format5 s.
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseries2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleUsing extreme gradient boosting to detect glottal closure instants in speech signalen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedglottal closure instant (GCI)en
dc.subject.translatedpitch marken
dc.subject.translateddetectionen
dc.subject.translatedclassificationen
dc.subject.translatedextreme gradient boostingen
dc.identifier.doi10.1109/ICASSP.2019.8683889
dc.type.statusPeer-revieweden
dc.identifier.document-number482554006149
dc.identifier.obd43927320
dc.project.IDGA19-19324S/Plně trénovatelná syntéza české řeči z textu s využitím hlubokých neuronových sítícs
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