  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 Amazon 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 Alexa skill 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.
