Effects of disfluent machine speech on memory recall in human-machine interaction

Xinyi Chen, Andreas Maria Liesenfeld, Shiyue Li, Yao Yao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In recent years, voice-AI systems have seen significant improvement in intelligibility and naturalness, but the human experience when talking to a machine is still remarkably different from the experience of talking to a fellow human. In this paper, we explore one dimension of such differences, i.e., the occurrence of disfluency in machine speech and how it may impact human listeners’ processing and memory of linguistic information. We conducted a humanmachine conversation task in Mandarin Chinese using a humanoid social robot (Furhat), with different types of machine speech (pre-recorded natural speech vs. synthesized speech, fluent vs. disfluent). During the task, the human interlocutor was tested in terms of how well they remembered the information presented by the robot. The results showed that disfluent speech (surrounded by “um”/”uh”) boosted memory retention only in pre-recorded speech for a retelling
task but not in synthesized speech. We discuss the implications of current findings and possible directions of future work.
Original languageEnglish
Title of host publicationProceedings of the Conference : Human Perspectives on Spoken Human-Machine Interaction
EditorsSarah Warchhold, Daniel Duran, Iona Gessinger, Eran Raveh
Pages52-57
DOIs
Publication statusPublished - Nov 2021
EventFRIAS Junior Researcher Conference on Human Perspectives on Spoken Human-Machine Interaction (SpoHuMa21) -
Duration: 15 Nov 202117 Nov 2021
https://freidok.uni-freiburg.de/data/223814

Conference

ConferenceFRIAS Junior Researcher Conference on Human Perspectives on Spoken Human-Machine Interaction (SpoHuMa21)
Period15/11/2117/11/21
Internet address

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