{"title":"情绪分布与分类对紧急呼叫中心呼叫处理的影响","authors":"M. Bojanic, V. Delić, Alexey Karpov","doi":"10.5937/telfor2102075b","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":37719,"journal":{"name":"Telfor Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Influence of emotion distribution and classification on a call processing for an emergency call center\",\"authors\":\"M. Bojanic, V. Delić, Alexey Karpov\",\"doi\":\"10.5937/telfor2102075b\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":37719,\"journal\":{\"name\":\"Telfor Journal\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Telfor Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5937/telfor2102075b\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Telfor Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5937/telfor2102075b","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Influence of emotion distribution and classification on a call processing for an emergency call center
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.
期刊介绍:
The TELFOR Journal is an open access international scientific journal publishing improved and extended versions of the selected best papers initially reported at the annual TELFOR Conference (www.telfor.rs), papers invited by the Editorial Board, and papers submitted by authors themselves for publishing. All papers are subject to reviewing. The TELFOR Journal is published in the English language, with both electronic and printed versions. Being an IEEE co-supported publication, it will follow all the IEEE rules and procedures. The TELFOR Journal covers all the essential branches of modern telecommunications and information technology: Telecommunications Policy and Services, Telecommunications Networks, Radio Communications, Communications Systems, Signal Processing, Optical Communications, Applied Electromagnetics, Applied Electronics, Multimedia, Software Tools and Applications, as well as other fields related to ICT. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies towards the information and knowledge society. The Journal provides a medium for exchanging research results and technological achievements accomplished by the scientific community from academia and industry.