Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/7248
DC FieldValueLanguage
dc.contributor.authorKosmopoulos, Dimitrios-
dc.contributor.authorVarvarigou, Theodora-
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2013-02-20T12:03:35Zen
dc.date.accessioned2013-05-17T05:34:06Z-
dc.date.accessioned2015-12-02T12:26:24Z-
dc.date.available2013-02-20T12:03:35Zen
dc.date.available2013-05-17T05:34:06Z-
dc.date.available2015-12-02T12:26:24Z-
dc.date.issued2008-
dc.identifier.citationICASSP 2008. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 1937-1940en_US
dc.identifier.isbn978-1-4244-1483-3 (print)-
dc.identifier.issn978-1-4244-1484-0 (online)-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/7248-
dc.description.abstractHidden Markov models using finite Gaussian mixture models as their hidden state distributions have been applied in modeling of time series that result from various noisy signals. Nevertheless, Gaussian mixture models are well-known to be highly intolerant to the presence of outliers within the fitting sets used for their estimation. Finite Student's-t mixture models have recently emerged as a heaviertailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit those merits of Student's-t mixture models, we introduce in this paper a novel hidden Markov chain model where the hidden state distributions are considered to be finite mixtures of multivariate Student's-t densities and we derive an algorithm for the model parameters estimation under a maximum likelihood framework. We apply this novel approach in automatic gesture recognition and we show that our model provides a substantial improvement in data representation performance and computational efficiency over the standard Gaussian modelen_US
dc.language.isoenen_US
dc.publisherIEEE Xploreen
dc.rights© 2008 IEEEen_US
dc.subjectAcousticsen_US
dc.subjectSpeechen_US
dc.subjectGaussian distributionen_US
dc.subjectMarkov processesen_US
dc.subjectRobustnessen_US
dc.subjectSignal processingen_US
dc.subjectMixturesen_US
dc.titleA robust approach towards sequential data modeling and its application in automatic gesture recognitionen_US
dc.typeBook Chapteren_US
dc.collaborationNational Technical University Of Athensen_US
dc.collaborationNCSR Demokritosen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_US
dc.identifier.doi10.1109/ICASSP.2008.4518015en_US
dc.dept.handle123456789/54en
cut.common.academicyear2019-2020en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
Show simple item record

SCOPUSTM   
Citations 50

2
checked on Jun 14, 2019

Page view(s) 10

103
Last Week
5
Last month
8
checked on Oct 17, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.