I Know You're Listening: Designing Visual Backchannels for Voice User Interfaces

Nonverbal cues are essential for natural communication, yet voice assistants often lack such features, limiting their effectiveness in social contexts like emotion journaling. This study investigates the impact of different visual backchannel feedback patterns on conversational flow and user perception with an icon-based voice assistant. Using a within-subject design, participants experienced three feedback conditions: regular, synchronized, and randomized. Quantitative results showed that minimal differences in visual patterns significantly influenced user perceptions, with synchronized and randomized feedback generally outperforming regular feedback. Qualitative findings emphasized individual differences, highlighting the need for customizable feedback to address diverse user needs. This study contributes to the design of inclusive, user-centered voice assistant interfaces for social applications.

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Publication Details

Published
2025-03-18
DOI
https://doi.org/10.1145/3708557.3716343
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
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article

I Know You're Listening: Designing Visual Backchannels for Voice User Interfaces

Yoonvin Park, Hayeon Song, Doha Kim
Social Robot Interaction and HRI
article

I Know You're Listening: Designing Visual Backchannels for Voice User Interfaces

Yoonvin Park, Hayeon Song, Doha Kim
article en

Abstract

Nonverbal cues are essential for natural communication, yet voice assistants often lack such features, limiting their effectiveness in social contexts like emotion journaling. This study investigates the impact of different visual backchannel feedback patterns on conversational flow and user perception with an icon-based voice assistant. Using a within-subject design, participants experienced three feedback conditions: regular, synchronized, and randomized. Quantitative results showed that minimal differences in visual patterns significantly influenced user perceptions, with synchronized and randomized feedback generally outperforming regular feedback. Qualitative findings emphasized individual differences, highlighting the need for customizable feedback to address diverse user needs. This study contributes to the design of inclusive, user-centered voice assistant interfaces for social applications.

Sungkyunkwan University (KR)
Openalex Percentile: Top 95%
Social Robot Interaction and HRI
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