1 research outputs found
Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
Article 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