  Interpersonal spoken communication is central to human interaction and the
exchange of information. Such interactive processes involve not only speech and
spoken language but also non-verbal cues such as hand gestures, facial
expressions, and nonverbal vocalization, that are used to express feelings and
provide feedback. These multimodal communication signals carry a variety of
information about the people: traits like gender and age as well as about
physical and psychological states and behavior. This work uses wearable
multimodal sensors to investigate interpersonal communication behaviors
focusing on speaking patterns among healthcare providers with a focus on
nurses. We analyze longitudinal data collected from $99$ nurses in a large
hospital setting over ten weeks. The results indicate that speaking pattern
differences across shift schedules and working units. Moreover, results show
that speaking patterns combined with physiological measures can be used to
predict affect measures and life satisfaction scores. The implementation of
this work can be accessed at https://github.com/usc-sail/tiles-audio-arousal.
