Call Center Stress Recognition with Person-Specific Models
Author(s)Morris, Robert Randall; Hernandez, Javier; Picard, Rosalind W.
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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 automat- ically 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 per- son, and of 73.41% when trained and tested on different people using the proposed adaptations to SVMs.
DepartmentMassachusetts Institute of Technology. Media Laboratory
Affective Computing and Intelligent Interaction
Springer Berlin / Heidelberg
Hernandez, Javier, Rob R. Morris, and Rosalind W. Picard. “Call Center Stress Recognition with Person-Specific Models.” Affective Computing and Intelligent Interaction. Ed. Sidney D’Mello et al. Vol. 6974. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 125-134.
Author's final manuscript