# Establishing Human-Robot Trust through Music-Driven Robotic Emotion Prosody and Gesture

arXiv: https://arxiv.org/abs/2001.05863 · HTML: https://mlsanity.com/paper/2001.05863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective](https://mlsanity.com/paper/2304.11293/) (2023)
- [Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning](https://mlsanity.com/paper/2106.02556/) (2021)

## Abstract (arXiv)

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.