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Interpersonal spoken communication is central to human interaction and theexchange of information. Such interactive processes involve not only speech andspoken language but also non-verbal cues such as hand gestures, facialexpressions, and nonverbal vocalization, that are used to express feelings andprovide feedback. These multimodal communication signals carry a variety ofinformation about the people: traits like gender and age as well as aboutphysical and psychological states and behavior. This work uses wearablemultimodal sensors to investigate interpersonal communication behaviorsfocusing on speaking patterns among healthcare providers with a focus onnurses. We analyze longitudinal data collected from $99$ nurses in a largehospital setting over ten weeks. The results indicate that speaking patterndifferences across shift schedules and working units. Moreover, results showthat speaking patterns combined with physiological measures can be used topredict affect measures and life satisfaction scores. The implementation ofthis work can be accessed at https://github.com/usc-sail/tiles-audio-arousal.

Exploring Workplace Behaviors through Speaking Patterns using Large-scale Multimodal Wearable Recordings: A Study of Healthcare Providers