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A Multimodal Approach to Improve Performance Evaluation of Call Center Agent.

Abdelrahman Ahmed1, Khaled Shaalan2, Sergio Toral1

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

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Summary

This study introduces three advanced models for call center agent performance evaluation, enhancing accuracy by combining speech and text analysis. The multimodal approach significantly improves classification compared to individual speech or text models.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Speech Processing

Background:

  • Call center agent performance evaluation is crucial for service quality.
  • Existing methods often lack comprehensive analysis of agent-customer interactions.
  • Integrating multimodal data (speech and text) offers potential for more accurate assessments.

Purpose of the Study:

  • To propose and evaluate three novel modeling techniques for enhanced call center agent performance evaluation.
  • To compare the effectiveness of speech-only, text-only, and multimodal approaches.
  • To introduce the Max Weights Similarity (MWS) function for improved classification accuracy.

Main Methods:

  • Speech processing using 65 features and an attention layer with the Open-Smile toolkit.
  • Implementing the Max Weights Similarity (MWS) function to replace Softmax for improved classification accuracy.
  • Developing a multimodal approach combining Convolutional Neural Networks (CNNs) and Bi-directional Long-Short Term Memory (BiLSTMs) for speech and text data.

Main Results:

  • The multimodal classification model demonstrated superior performance.
  • An improvement of 0.22% was observed compared to the acoustic (speech) model.
  • An improvement of 1.7% was observed compared to the text model.

Conclusions:

  • The proposed multimodal approach, integrating speech and text analysis, significantly enhances call center agent performance evaluation.
  • The Max Weights Similarity (MWS) function contributes to improved classification accuracy.
  • Combining CNNs and BiLSTMs in a multimodal framework offers a robust solution for analyzing agent interactions.