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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F14%3A86090869%21RIV15-MSM-27240___
rdf:type
n5:Vysledek skos:Concept
dcterms:description
Emotional state classification of human speech and recognition accuracy of the classifiers is disclosed in this paper. Recent developments in speech recognition places more emphasis on the extraction of information about the speech source. This means obtain information about who and how it was said. This article describes research which seeks to recognize the information from speaking, emotional state in particular. Emotional state is recognized by using different classifiers and features of speech by nowadays known systems. Berlin database of emotional recordings was used to train and test the system. Mel-frequency spectral coefficients and dynamic coefficients were extracted from the audio signal of the database. For classification were used Gaussian Mixture Model, k-Nearest Neighbours and Artificial Neural Networks methods. The main effort of this research is to examine the accuracy and usability of classifying methods for detection of human stress status from his speech. Emotional state classification of human speech and recognition accuracy of the classifiers is disclosed in this paper. Recent developments in speech recognition places more emphasis on the extraction of information about the speech source. This means obtain information about who and how it was said. This article describes research which seeks to recognize the information from speaking, emotional state in particular. Emotional state is recognized by using different classifiers and features of speech by nowadays known systems. Berlin database of emotional recordings was used to train and test the system. Mel-frequency spectral coefficients and dynamic coefficients were extracted from the audio signal of the database. For classification were used Gaussian Mixture Model, k-Nearest Neighbours and Artificial Neural Networks methods. The main effort of this research is to examine the accuracy and usability of classifying methods for detection of human stress status from his speech.
dcterms:title
Classification Methods Accuracy for Speech Emotion Recognition System Classification Methods Accuracy for Speech Emotion Recognition System
skos:prefLabel
Classification Methods Accuracy for Speech Emotion Recognition System Classification Methods Accuracy for Speech Emotion Recognition System
skos:notation
RIV/61989100:27240/14:86090869!RIV15-MSM-27240___
n3:aktivita
n4:S
n3:aktivity
S
n3:dodaniDat
n18:2015
n3:domaciTvurceVysledku
n12:8051283 Partila, Pavol n12:4574834 n12:7890230
n3:druhVysledku
n19:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n7:predkladatel
n3:idSjednocenehoVysledku
7445
n3:idVysledku
RIV/61989100:27240/14:86090869
n3:jazykVysledku
n21:eng
n3:klicovaSlova
System; Recognition; Emotion; Speech; Accuracy; Methods; Classification
n3:klicoveSlovo
n6:Classification n6:Methods n6:System n6:Recognition n6:Emotion n6:Accuracy n6:Speech
n3:kontrolniKodProRIV
[CCC876FFDE07]
n3:mistoKonaniAkce
Ostrava
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Nostradamus 2014: prediction, modeling and analysis of complex systems
n3:obor
n16:JC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n18:2014
n3:tvurceVysledku
Partila, Pavol Šafařík, Jakub Továrek, Jaromír Vozňák, Miroslav
n3:typAkce
n10:WRD
n3:zahajeniAkce
2014-06-23+02:00
s:issn
2194-5357
s:numberOfPages
9
n20:doi
10.1007/978-3-319-07401-6_44
n8:hasPublisher
Springer-Verlag
n17:isbn
978-3-319-07400-9
n11:organizacniJednotka
27240