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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2012 - 5

The Analysis of the FCM and WKNN Algorithms Performance for the Emotional Corpus SROL

ZBANCIOC, M. See more information about ZBANCIOC, M. on SCOPUS See more information about ZBANCIOC, M. on IEEExplore See more information about ZBANCIOC, M. on Web of Science, FERARU, S. M. See more information about FERARU, S. M. on SCOPUS See more information about FERARU, S. M. on SCOPUS See more information about FERARU, S. M. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (875 KB) | Citation | Downloads: 344 | Views: 2,128

Author keywords
emotional speech database, FCM and WKNN algorithm, recurrent coefficient, statistical parameters

References keywords
speech(20), emotion(15), recognition(11), systems(7), fuzzy(7), features(7), classification(7), teodorescu(6), emotional(5), communication(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 33 - 38
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03005
Web of Science Accession Number: 000308290500005
SCOPUS ID: 84865856327

Abstract
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The purpose of this research is to find a set of relevant parameters for the emotion recognition. In this study we used the recordings from the emotion database SROL which is part of the project 'Voiced Sounds of Romanian Language'. The database was validated by human listeners. The recognition accuracy of the correct expressed emotion (neutral tone, joy, fury and sadness) for the entire database was 63.97%. We used for the classification of input data the Recurrent Fuzzy C-Means (FCM) and WKNN algorithms. We compared the cluster position with the statistical parameters extracted from vowels in order to establish the relevance of each parameter in the recognition of the emotions. For the extracted parameters for each vowel (mean, median and standard deviation of fundamental frequency - F0 and F1-F4 formants, jitter, and shimmer) the FCM algorithm gave satisfactory results in the phonemes recognition, but not to the emotions. For this reason we used WKNN algorithm in classification, which provided the errors around 20-30% comparing with FCM algorithm when the classification errors are around 40-50%.


References | Cited By

Cited-By ISI Web of Science

Web of Science® Times Cited: 9 [View]
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Cited-By CrossRef

SCOPUS® Times Cited: 9
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[Free preview]

Updated 3 days, 6 hours ago

Cited-By CrossRef

[1] Emotion recognition in Romanian language using LPC features, Feraru, Silvia Monica, Dan Zbancioc, Marius, 2013 E-Health and Bioengineering Conference (EHB), ISBN 978-1-4799-2373-1, 2013.
Digital Object Identifier: 10.1109/EHB.2013.6707314
[CrossRef]

[2] A study about MFCC relevance in emotion classification for SRoL database, Dan, Zbancioc Marius, Monica, Feraru Silvia, 2013 4th International Symposium on Electrical and Electronics Engineering (ISEEE), ISBN 978-1-4799-2442-4, 2013.
Digital Object Identifier: 10.1109/ISEEE.2013.6674323
[CrossRef]

[3] Speech emotion recognition for SROL database using weighted KNN algorithm, Feraru, Monica, Zbancioc, Marius, Proceedings of the International Conference on ELECTRONICS, COMPUTERS and ARTIFICIAL INTELLIGENCE - ECAI-2013, ISBN 978-1-4673-4937-6, 2013.
Digital Object Identifier: 10.1109/ECAI.2013.6636198
[CrossRef]

[4] Comparative analysis between SROL - Romanian database and Emo - German database, Feraru, Silvia Monica, Zbancioc, Marius Dan, 2015 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-4673-7488-0, 2015.
Digital Object Identifier: 10.1109/ISSCS.2015.7204015
[CrossRef]

[5] An overview of several researches on fuzzy logic in intelligent systems, Luca, Mihaela, Luca, Ramona, Bejinariu, Silviu-Ioan, Ciobanu, Adrian, Paduraru, Otilia, Zbancioc, Marius, Barbu, Tudor, 2015 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-4673-7488-0, 2015.
Digital Object Identifier: 10.1109/ISSCS.2015.7204019
[CrossRef]

Updated 3 days, 6 hours ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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