  Cancer impacts the quality of life of those diagnosed as well as their spouse
caregivers, in addition to potentially influencing their day-to-day behaviors.
There is evidence that effective communication between spouses can improve
well-being related to cancer but it is difficult to efficiently evaluate the
quality of daily life interactions using manual annotation frameworks.
Automated recognition of behaviors based on the interaction cues of speakers
can help analyze interactions in such couples and identify behaviors which are
beneficial for effective communication. In this paper, we present and detail a
dataset of dyadic interactions in 85 real-life cancer-afflicted couples and a
set of observational behavior codes pertaining to interpersonal communication
attributes. We describe and employ neural network-based systems for classifying
these behaviors based on turn-level acoustic and lexical speech patterns.
Furthermore, we investigate the effect of controlling for factors such as
gender, patient/caregiver role and conversation content on behavior
classification. Analysis of our preliminary results indicates the challenges in
this task due to the nature of the targeted behaviors and suggests that
techniques incorporating contextual processing might be better suited to tackle
this problem.
