Abstract

Accurate ground truth representations of human behavior and experiences are essential for furthering our understanding of the complex relationships between everyday events and interactions and their effects on people. Producing accurate ground truth signals for subjective or latent experiences is difficult because it requires human annotation and is subject to annotator bias, distraction artifacts, valuation errors, among others. We build on previous work aiming to produce highly accurate continuous-scale ground truth labels for human experiences which advocates using supplemental human observations to warp the continuous-scale annotations to correct these errors. We propose a new method, trapezoidal segmented regression, for optimally approximating fused human-produced continuous-scale annotations to simplify its segmentation into intervals of low and high confidence in valuation. We evaluate this …

Date

2019

Authors

Brandon M Booth, Shrikanth S Narayanan

Conference

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

600-606

Publisher

IEEE