Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Article numbre 5489

Detalhes bibliográficos
Autores: Ahmed, Abdelrahman, Toral, S. L., Shaalan, Khaled, Hifny, Yaser
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2020
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/103080
Acesso em linha:https://hdl.handle.net/11441/103080
https://doi.org/10.3390/s20195489
Access Level:Acceso aberto
Palavra-chave:Productivity modeling
LSTMs
CNNs
Attention layer
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spelling Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural NetworksAhmed, AbdelrahmanToral, S. L.Shaalan, KhaledHifny, YaserProductivity modelingLSTMsCNNsAttention layerArticle numbre 5489Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.MDPI AGIngeniería ElectrónicaTIC-201: ACE-TI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/103080https://doi.org/10.3390/s20195489reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSensors, 20 (19), 1-11.https://www.mdpi.com/1424-8220/20/19/5489info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1030802026-06-17T12:51:07Z
dc.title.none.fl_str_mv Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
title Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
spellingShingle Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
Ahmed, Abdelrahman
Productivity modeling
LSTMs
CNNs
Attention layer
title_short Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
title_full Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
title_fullStr Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
title_full_unstemmed Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
title_sort Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
dc.creator.none.fl_str_mv Ahmed, Abdelrahman
Toral, S. L.
Shaalan, Khaled
Hifny, Yaser
author Ahmed, Abdelrahman
author_facet Ahmed, Abdelrahman
Toral, S. L.
Shaalan, Khaled
Hifny, Yaser
author_role author
author2 Toral, S. L.
Shaalan, Khaled
Hifny, Yaser
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Electrónica
TIC-201: ACE-TI
dc.subject.none.fl_str_mv Productivity modeling
LSTMs
CNNs
Attention layer
topic Productivity modeling
LSTMs
CNNs
Attention layer
description Article numbre 5489
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/103080
https://doi.org/10.3390/s20195489
url https://hdl.handle.net/11441/103080
https://doi.org/10.3390/s20195489
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, 20 (19), 1-11.
https://www.mdpi.com/1424-8220/20/19/5489
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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