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

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dc.contributor.authorAhmed , Abdelrahman
dc.contributor.authorToral, Sergio
dc.contributor.authorShaalan, Khaled
dc.contributor.authorHifny, Yaser
dc.date.accessioned2025-05-14T12:43:35Z
dc.date.available2025-05-14T12:43:35Z
dc.date.issued2020
dc.description.abstractMeasuring 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.
dc.identifier.citationAhmed A. et al. (2020) “Agent productivity modeling in a call center domain using attentive convolutional neural networks,” Sensors (Switzerland), 20(19), pp. 1–11.
dc.identifier.doihttps://doi.org/10.3390/s20195489.
dc.identifier.issn1424-8220
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3033
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofseriesSensors (Switzerland)v20 n19 (2020 10 01): 1-11
dc.subjectproductivity modeling; LSTMs; CNNs; attention layer
dc.titleAgent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
dc.typeArticle
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