Translating Silence: Investigation of Unspoken Meaning and Intent in AI-Mediated Multilingual Communication

Abstract

Silence, commonly understood as the absence of speech, is viewed in the study of communication as a significant and strategic attribute, with unspoken meaning, pragmatic indicators, and culturally specific signification. The research methodology used in this study is qualitative. Using the guide of the relevance theory (Sperber & Wilson, 1986), the research is qualitative in nature, namely, it applies the document analysis and content analysis approach to secondary sources, such as academic articles, books, and institutional reports, to understand to what extent AI systems could be used to process unspoken meaning. The study aims to discuss the conceptualisation of silence, evaluate the accuracy of AI-mediated translation and communication, and examine whether AI interpretation aligns with relevance-based pragmatic principles. Results indicate that AI systems are often based on explicit textual input and probabilistic models, and that silence is frequently ignored or misinterpreted, distorting communication intent, especially in high-context cultural communication, where silence is a sign of respect or hesitation, or even of indirect approval. The article also illustrates that AI's inability to conduct contextual inference and pragmatic reasoning creates systematic discrepancies between human interpretation and machine generated results, thereby reflecting the limitations of cross-cultural and multilingual communication through technology. The study has a theoretical and practical contribution as it illuminates the importance of unspoken meaning in human communication, how cognitive pragmatic frameworks can be used to design AI systems, and provides suggestions on how context sensitive and culturally sensitive AI models can be developed and how they can maintain implicit intent and improve communicative fidelity during interactions involving multiple languages.