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2021, vol. 13, br. 2, str. 75-80
Influence of emotion distribution and classification on a call processing for an emergency call center
(naslov ne postoji na srpskom)
aUniverzitet u Novom Sadu, Fakultet tehničkih nauka
bRussian Academy of Sciences, St. Petersburg Institute for Informatics and Automation, St. Petersburg, Russia

e-adresamilana.bojanic@uns.ac.rs, vlado.delic@uns.ac.rs, karpov@iias.spb.su
Projekat:
This research was supported by the Science Fund of the Republic of Serbia, (grant #6524560, AI-S-ADAPT)
Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije (institucija: Univerzitet u Novom Sadu, Fakultet tehničkih nauka) (MPNTR - 451-03-68/2020-14/200156)

Ključne reči: affective computing; call center; speech emotion recognition
Sažetak
(ne postoji na srpskom)
The article addresses the influence of two aspects on speech emotion recognition utilization for an emergency call center: a frequency of a caller experiencing certain emotional state and classification methods used for speech emotion recognition. In situations when more simultaneous calls in an emergency call center are received, the aim is to detect more urgent callers, e.g. in a life threating situation, and give them priority in a callers' queue. Three different emotion distributions based on the corpora from real-world emergency call centers are considered. The influence of those emotion distributions on the proposed call redistribution and subsequent time savings are reported and discussed. Regarding speech emotion classification, two approaches are presented, namely the linear Bayes classifier and a multilayer perceptron-based neural network. Their recognition results on the corpus of acted emotional Serbian speech are presented and potential application in an emergency call center is discussed.
Reference
Abbaschian, B.J., Sierra-Sosa, D., Elmaghraby, A. (2021) Deep learning techniques for speech emotion recognition, from databases to models. Sensors, 21(4): 1249
Akçay, M.B., Oğuz, K. (2020) Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Communication, 116: 56-76
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Bojanić, M., Delić, V., Karpov, A. (2020) Call redistribution for a call center based on speech emotion recognition. Applied Sciences, 10(13): 4653
Bojanić, M., Crnojević, V., Delić, V. (2012) Application of neural networks in emotional speech recognition. u: 11th Symposium on Neural Network Applications in Electrical Engineering, Belgrade, 223-226
Bojanić, M., Delić, V., Karpov, A. (2020) Effect of Emotion Distribution on a Call Processing for an Emergency Call Center. u: 28th Telecommunications Forum (TELFOR), Belgrade, 1-4
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Jovičić, S.T., Kašić, Z., Đorđević, M., Rajković, M. (2004) Serbian emotional speech database: Design, processing and evaluation. u: Proc. 9th Int. Conf. SPECOM'2004, Russia, 77-81
Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T. (2019) Speech emotion recognition using deep learning techniques: A review. IEEE Access, 7: 117327-117345
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O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/telfor2102075B
primljen: 26.05.2021.
prihvaćen: 11.11.2021.
objavljen: 30.12.2021.
objavljen u SCIndeksu: 18.01.2022.

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