The present contribution provides a theoretical anchor for a rapidly developing and expanding body of research in the field of human-machine interaction (HMI). While HMI is often described as inherently heterogeneous (cf. Zelou and Halliday 2024, Lotze 2025), communication between human interlocutors is heterogeneous as well, and speech accommodation processes are not uniquely applicable to HMI but are a characteristic feature of speech production in humans (Giles at el. 1991). Psycholinguistic research suggests that the underlying mechanisms of speech production remain the same across contexts. According to Levelt (1989), speakers produce utterances in three steps: conceptualization, formulation, and articulation. During these stages, a preverbal message is planned, lexically and grammatically encoded, and articulated as overt speech, while a monitoring mechanism allows speakers to detect and correct errors. Due to the universal nature of the process of speech production in humans, we can assume that users go through the same levels of speech production when interacting with different voice-based artificial interlocutor types, entering the conversations with different goals (Gambino & Liu 2022).
Building on this framework, we compare three types of voice-based conversational systems: voice assistants (e.g., Amazon Alexa), LLM-based assistants (e.g., ChatGPT), and customer service voice bots. Differences in system design and interaction context make the conceptualization stage distinct across these systems, leading to variation in users’ speech planning and production. Thus, while the underlying processes remain uniform, the resulting utterances are heterogeneous. By examining these differences, the study highlights how interaction context and system design shape spoken utterances. From a practical conversational AI design perspective, these insights are relevant for omnichannel conversational design and can inform further decisions such as system’s barge-in behavior, no-input timeouts, turn-taking strategies, as well as prompt design.

Literature:
Giles, H., Coupland, N., & Coupland, J. (1991). Accommodation theory: Communication, context, and consequence. Contexts of accommodation: Developments in applied sociolinguistics, 1(1), 68.
Gambino, A., & Liu, B. (2022). Considering the context to build theory in HCI, HRI, and HMC: Explicating differences in processes of communication and socialization with social technologies. Human-Machine Communication, 4, 111-130.
Levelt, W. J. M. (1989). Speaking: From intention to articulation. Cambridge, MA: MIT Press/Bradford Books.
Lotze, Netaya (2025): Human-Machine Interaction as a Complex Socio-Linguistic Practice. In: Stephan Habscheid und Tim-Moritz Hector (Hrsg.). Voice Assistants.transcript.
Zellou, G., & Holliday, N. (2024). Linguistic analysis of human-computer interaction. Frontiers in Computer Science, 6, 1384252.