  As human-robot collaboration opportunities continue to expand, trust becomes
ever more important for full engagement and utilization of robots. Affective
trust, built on emotional relationship and interpersonal bonds is particularly
critical as it is more resilient to mistakes and increases the willingness to
collaborate. In this paper we present a novel model built on music-driven
emotional prosody and gestures that encourages the perception of a robotic
identity, designed to avoid uncanny valley. Symbolic musical phrases were
generated and tagged with emotional information by human musicians. These
phrases controlled a synthesis engine playing back pre-rendered audio samples
generated through interpolation of phonemes and electronic instruments.
Gestures were also driven by the symbolic phrases, encoding the emotion from
the musical phrase to low degree-of-freedom movements. Through a user study we
showed that our system was able to accurately portray a range of emotions to
the user. We also showed with a significant result that our non-linguistic
audio generation achieved an 8% higher mean of average trust than using a
state-of-the-art text-to-speech system.
