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

Ahmed , Abdelrahman; Toral,  Sergio; Shaalan, Khaled; Hifny, Yaser

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

Date

2020

Authors

Publisher

MDPI

Abstract

Measuring 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.

Keywords

productivity modeling; LSTMs; CNNs; attention layer

Citation

Ahmed A. et al. (2020) “Agent productivity modeling in a call center domain using attentive convolutional neural networks,” Sensors (Switzerland), 20(19), pp. 1–11.