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Call Center Stress Recognition with Person-Specific Models

  • Javier Hernandez
  • Rob R. Morris
  • Rosalind W. Picard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6974)

Abstract

Nine call center employees wore a skin conductance sensor on the wrist for a week at work and reported stress levels of each call. Although everyone had the same job profile, we found large differences in how individuals reported stress levels, with similarity from day to day within the same participant, but large differences across the participants. We examined two ways to address the individual differences to automatically recognize classes of stressful/non-stressful calls, namely modifying the loss function of Support Vector Machines (SVMs) to adapt to the varying priors, and giving more importance to training samples from the most similar people in terms of their skin conductance lability. We tested the methods on 1500 calls and achieved an accuracy across participants of 78.03% when trained and tested on different days from the same person, and of 73.41% when trained and tested on different people using the proposed adaptations to SVMs.

Keywords

Stress recognition skin conductance interpersonal variability Support Vector Machines Affective Computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Javier Hernandez
    • 1
  • Rob R. Morris
    • 1
  • Rosalind W. Picard
    • 1
  1. 1.Media LabMassachussets Institute of TechnologyCambridgeUSA

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