Spoken dialogue systems have transformed human-machine interaction by providing real-time responses to queries. However, misunderstandings between the user and system persist. This study explores the significance of interactional language in dialogue repair between virtual assistants and users by analyzing interactions with Google Assistant and Siri, focusing on their utilization and response to the other-initiated repair strategy "huh?" prevalent in human-human interaction. Findings reveal several assistant-generated strategies but an inability to replicate human-like repair strategies such as "huh?". English and Spanish user acceptability surveys show differences in users' repair strategy preferences and assistant usage, with both similarities and disparities among the two surveyed languages. These results shed light on inequalities between interactional language in human-human interaction and human-machine interaction, underscoring the need for further research on the impact of interactional language in human-machine interaction in English and beyond.

Language speakers often use what are known as repair initiators to mend fundamental disconnects that occur between them during verbal communication. Previous research in this field has mainly focused on the human-to-human use of repair initiator. We proposed an examination of dialogue repair structure wherein the dialogue initiator is human and the party that initiates or responds to the repair is a virtual assistant. This study examined the use of repair initiators in both English and Spanish with two popular assistants, Google Assistant and Apple's Siri. Our aim was to codify the differences, if any, in responses by voice assistants to dialogues in need of repair as compared to human-human dialogues also in need of repair. Ultimately the data demonstrated that not only were there differences between human-assistant and human-human dialogue repair strategies, but that there were likewise differences among the assistants and the languages studied.

Speakers frequently repair their speech, and listeners must therefore integrate information across ill-formed, often fragmentary inputs. Previous dialogue systems for human-robot interaction (HRI) have addressed certain problems in dialogue repair, but there are many problems that remain. In this paper, we discuss these problems from the perspective of Conversation Analysis, and argue that a more holistic account of dialogue repair will actually aid in the design and implementation of machine dialogue systems.