Abstract

Empathy is an important aspect of social communication, especially in medical and psychotherapy applications. Measures of empathy can offer insights into the quality of therapy. We use an N-gram language model based maximum likelihood strategy to classify empathic versus non-empathic utterances and report the precision and recall of classification for various parameters. High recall is obtained with unigram while bigram features achieved the highest F1-score. Based on the utterance level models, a group of lexical features are extracted at the therapy session level. The effectiveness of these features in modeling session level annotator perceptions of empathy is evaluated through correlation with expert-coded session level empathy scores. Our combined feature set achieved a correlation of 0.56 between predicted and expert-coded empathy scores. Results also suggest that the longer term empathy …

Date

2012

Authors

Bo Xiao, Dogan Can, Panayiotis G Georgiou, David Atkins, Shrikanth S Narayanan

Conference

Proceedings of the 2012 Asia pacific signal and information processing association annual summit and conference

Pages

1-4

Publisher

IEEE