  Empathy involves understanding other people's situation, perspective, and
feelings. In clinical interactions, it helps clinicians establish rapport with
a patient and support patient-centered care and decision making. Understanding
physician communication through observation of audio-recorded encounters is
largely carried out with manual annotation and analysis. However, manual
annotation has a prohibitively high cost. In this paper, a multimodal system is
proposed for the first time to automatically detect empathic interactions in
recordings of real-world face-to-face oncology encounters that might accelerate
manual processes. An automatic speech and language processing pipeline is
employed to segment and diarize the audio as well as for transcription of
speech into text. Lexical and acoustic features are derived to help detect both
empathic opportunities offered by the patient, and the expressed empathy by the
oncologist. We make the empathy predictions using Support Vector Machines
(SVMs) and evaluate the performance on different combinations of features in
terms of average precision (AP).
