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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
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.
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.
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