Toward Enabling Natural Conversation with Older Adults via the Design of LLM-Powered Voice Agents that Support Interruptions and Backchannels

要旨

Voice agents can construct meaningful conversations with older adults to offer various benefits, such as providing emotional companionship and assisting with memory recall. However, such conversations often follow the simple turn-taking pattern and lack interruption and backchannel of natural human conversation. Previous research has shown that this rigid turn-taking pattern lacks interactivity and initiative, limiting the flexible communication between older adults and voice agents. To address these issues and create a more natural conversational voice agent, we first conducted a formative study to identify common usage of interruption in the natural conversations of older adults. We then designed an LLM-powered Barge-in agent that supports interruption and backchannel. Our within-subject exploratory study showed that participants felt that conversations with Barge-in agents were more natural, engaging, and fluent than with the No barge-in agent. We further present design implications for creating more natural and human-like voice agents for older adults.

著者
Chao LIU
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Mingyang Su
Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
Yan Xiang
Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Yuru Huang
The Hong Kong University of Science and Technology (Guangzhou), Guangdong, China
Yiqian Yang
HKUST(GZ), guangzhou, Guangdong, China
Kang Zhang
Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
Mingming Fan
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
DOI

10.1145/3706598.3714228

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714228

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Communication and Social Interaction

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7 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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