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Listening to your inner voices: investigating means for voice notifications

Our research investigates notification qualities of different types of voices, moving toward interfaces that support optimal allocation of attention to maximize system utility. We conducted an experiment to determine the interruption, reaction, and comprehension values of three different voice categories: the user's voice, a familiar voice, and an unfamiliar voice. Initial testing showed significant and impactful results: unfamiliar voices are the least interruptive, and a user reacts most quickly to one's own voice. Motivated by these findings, we report on the development and deployment of a notification system that exploits the differences in familiarity of a voice.