Abstract

Human annotations are of integral value in human behavior studies and in particular for the generation of ground truth for behavior prediction using various machine learning methods. These often subjective human annotations are especially required for studies involving measuring and predicting hidden mental states (e.g. emotions) that cannot effectively be measured or assessed by other means. Human annotations are noisy and prone to the influence of several factors including personal bias, task ambiguity, environmental distractions, and health state. We propose a novel method for fusion of continuous real-time human annotations to generate accurate ground truth estimates. We introduce a signal warping method that uses additional comparative rank-based information about specific subsets of the annotations to correct for specific types of human annotation artifacts. This approach is validated using a …

Date

April 15, 2018

Authors

Brandon M Booth, Karel Mundnich, Shrikanth S Narayanan

Conference

2018 IEEE international conference on acoustics, speech and signal processing (icassp)

Pages

3091-3095

Publisher

IEEE