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Machine learning driven musical improvisation for mechanomorphic human-robot interaction

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

As industrial robots and social robots become prevalent in commercial and home settings it is crucial to improve forms of communication with human collaborators and companions. In this work, I describe the use of musical improvisation to generate emotional musical prosody for improved human-robot interaction. This aims to develop a canny approach, where robots perform in a mechanomorphic manner improving collaboration opportunities with humans. I have currently collected a new 12-hour dataset and developed a Conditional Variational Autoencoder to generate new phrases. Generations have then been used to compare the impact of prosody on anthropomorphism, animacy, likeability, perceived intelligence, and trust. Future work will incorporate prosody into groups of robots and humans, using personality to drive emotional decisions and emotion contagion.

Original languageEnglish
Title of host publicationHRI 2021 - Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
EditorsCinthy Bethel, Ana Paiya, Elizabeth Broadbent, David Feil-Seifer, Daniel Szafir
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages559-561
Number of pages3
ISBN (Electronic)9781450382908
DOIs
Publication statusPublished - 8 Mar 2021
Externally publishedYes
Event2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021 - Virtual, Online, United States
Duration: 8 Mar 202111 Mar 2021

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period8/03/2111/03/21

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