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The Analysis of the FCM and WKNN Algorithms Performance for the Emotional Corpus SROLZBANCIOC, M. , FERARU, S. M.
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emotional speech database, FCM and WKNN algorithm, recurrent coefficient, statistical parameters
speech(20), emotion(15), recognition(11), systems(7), fuzzy(7), features(7), classification(7), emotional(5), communication(5), automatic(5)
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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
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%.
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 Call Redistribution for a Call Center Based on Speech Emotion Recognition, Bojanić, Milana, Delić, Vlado, Karpov, Alexey, Applied Sciences, ISSN 2076-3417, Issue 13, Volume 10, 2020.
Digital Object Identifier: 10.3390/app10134653 [CrossRef]
 Recognizing Fear / Anxiety in Relation to Other Emotions, Marius-Dan, Zbancioc, Monica, Feraru, 2020 International Conference on e-Health and Bioengineering (EHB), ISBN 978-1-7281-8803-4, 2020.
Digital Object Identifier: 10.1109/EHB50910.2020.9280292 [CrossRef]
 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]
 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]
 Using local variance, Allan- and Hadamard variances in speech analysis – Pitch analysis, Teodorescu, Horia-Nicolai, 2019 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-7281-3896-1, 2019.
Digital Object Identifier: 10.1109/ISSCS.2019.8801757 [CrossRef]
 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]
 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]
 Deep Learning Neural Architecture in Emotion Recognition for Romanian Language, Zbancioc, MD., Feraru, SM., 2019 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-7281-3896-1, 2019.
Digital Object Identifier: 10.1109/ISSCS.2019.8801782 [CrossRef]
 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]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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