THE IMPACT OF VOICE ASSISTANTS ON HUMAN-COMPUTER INTERACTION

Abstract

The present research investigates the influence of voice assistants on the relationship between computers and users through the lens of language. Within the context of the relationship between users and the computer, the influence of voice-activated technology on the communication framework is examined. This covers the speech acts, the structure of the conversation, and pragmatics.  The primary aim is to look into the change in users' communication patterns and determine the degree of fluency of such patterns. This investigation is positioned within the mixed research paradigm where quantitative and qualitative approaches are integrated. Data was collected through 200 participants who interacted with predominant voice assistants such as Siri, Alexa, and Google Assistant. This study utilized a conversation analytic framework to analyze 500 recorded dialogues, and integrated users' surveys on satisfaction and perception of naturalness in the interaction. Arguments for the analysis incorporated patterns of speech, mechanisms of turn taking, conversation repairs, and sequence analysis of the discourse.  The study highlights the pragmatic and syntactic changes users made in the context of interaction with computers. Analyses of the collected data indicated that a considerable number of users, more than 76%, demonstrate the declining complexity of grammar and less than 64% expressed the mildly frustrating condition of a voice assistant that misunderstood the context. Comparatively, users engaging computer systems made shorter, more direct speeches with a marked reduction of indirect speech compared to a conversation with another person. The current study reveals the ways voice assistants reshaped the communicative standards and expectations in human-computer interaction. It suggested refinements in sociolinguistic variation and contextual understanding, the incorporation of more advanced natural language processing features, and the introduction of human-like conversational backoff strategies to make the voice interaction more seamless and natural.