Enhancing Usability of Voice Interfaces for Socially Assistive Robots Through Deep Learning: A German Case Study

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

Abstract

Voice Interfaces have become ubiquitous as they can make complex technology more usable and accessible. Current voice interfaces, however, often require the user to learn specific speech commands or sentence patterns to use them. This property does not satisfy usability heuristics and causes current language interfaces to underachieve the naturalness of language interaction. To address this issue, we developed a voice interface that is capable of understanding natural everyday language. The overall objective is to build a German language voice interface for socially assistive robots that can work in public spaces. Therefore, we cannot assume the user’s prior knowledge or experience. Based on recent advances in deep natural language processing, we have built a voice interface that is not restricted to specific speech commands. To test this voice interface, we conducted a study with 47 participants. Results indicate 93% of the given tasks were solved successfully by the target user group without prior training or experience with the voice interface.

Details

Original languageEnglish
Title of host publicationArtificial Intelligence in HCI - 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
EditorsHelmut Degen, Stavroula Ntoa
PublisherSpringer, Cham
Pages231-249
Number of pages19
Volume2
ISBN (electronic)978-3-031-60615-1
ISBN (print)978-3-031-60614-4
Publication statusPublished - 2024
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science
Volume14736
ISSN0302-9743

External IDs

ORCID /0000-0002-1890-4281/work/161409139
Scopus 85195442937
Mendeley 13aa310d-db94-3a60-82e9-9a7534edaf37

Keywords

Keywords

  • Human-Robot Interaction, User Study, Voice Interface