arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.02421cs.HC
收藏

老年人使用语音助手交互中错误的情境化理解:一项为期一个月的入户研究

Situated Understanding of Errors in Older Adults' Interactions with Voice Assistants: A Month-Long, In-Home Study

Amama Mahmood, Junxiang Wang, Chien-Ming Huang

AI总结:

本研究通过入户音频记录分析老年人使用语音助手的错误反应,并测试ChatGPT的改进效果,提出结合LLMs上下文能力以优化错误预防与管理的设计建议。

摘要:

我们的工作旨在解决老年人使用商业语音助手(VAs)时面临的挑战,特别是在对话中断和错误处理方面。传统的用户体验收集方法——使用日志和事后访谈——无法完全捕捉老年人与VAs交互的复杂性,尤其是他们对错误的反应。为了弥补这一差距,我们为15位老年人的家中配备了集成自定义录音设备的智能音箱,以收集“真实环境”下的音频交互数据,进行详细的错误分析。认识到当前VAs在对话方面的局限性,本研究还探索了大型语言模型(LLMs)处理自然且不完美文本的能力,以改进VAs。在研究中期,我们部署了基于ChatGPT的VA,以调查其对老年人的有效性。我们的研究表明,应利用语音和口头回应结合LLMs的上下文能力来增强VAs中的错误预防和管理,同时提出设计建议,使VA的能力与老年人的期望相一致。

英文摘要:

Our work addresses the challenges older adults face with commercial Voice Assistants (VAs), notably in conversation breakdowns and error handling. Traditional methods of collecting user experiences-usage logs and post-hoc interviews-do not fully capture the intricacies of older adults' interactions with VAs, particularly regarding their reactions to errors. To bridge this gap, we equipped 15 older adults' homes with smart speakers integrated with custom audio recorders to collect "in-the-wild" audio interaction data for detailed error analysis. Recognizing the conversational limitations of current VAs, our study also explored the capabilities of Large Language Models (LLMs) to handle natural and imperfect text for improving VAs. Midway through our study, we deployed ChatGPT-powered VA to investigate its efficacy for older adults. Our research suggests leveraging vocal and verbal responses combined with LLMs' contextual capabilities for enhanced error prevention and management in VAs, while proposing design considerations to align VA capabilities with older adults' expectations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑