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Related Experiment Video

Updated: Dec 7, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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

Abdelrahman Ahmed1, Sergio Toral1, Khaled Shaalan2

  • 1Department of Electronics Engineering, University of Seville, 41092 Seville, Spain.

Sensors (Basel, Switzerland)
|September 30, 2020
PubMed
Summary

This study introduces an objective framework using speech signal processing to measure call center agent productivity, outperforming text-based methods. The deep learning approach offers significant improvements for real estate call center evaluations.

Keywords:
CNNsLSTMsattention layerproductivity modeling

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Area of Science:

  • Artificial Intelligence
  • Speech Signal Processing
  • Machine Learning

Background:

  • Call center agent productivity is typically measured subjectively.
  • Existing evaluation systems lack objective metrics.

Discussion:

  • This research proposes an objective framework for modeling agent productivity in real estate call centers.
  • The framework utilizes speech signal processing and deep learning for binary classification.
  • Evaluated models include Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and attention mechanisms.

Key Insights:

  • The speech-based approach achieved a 1.57% absolute improvement over a text baseline.
  • Deep learning models demonstrate effectiveness in classifying agent productivity from speech.
  • The corpus comprised seven hours of annotated data from three call centers.

Outlook:

  • This objective framework can enhance the evaluation of call center agent performance.
  • Future work may involve larger datasets and diverse call center domains.
  • Speech analytics offers a promising avenue for objective performance measurement.