Strategies for Handling Barge-in Interruptions in Conversational AI Interfaces

Abstract

The article addresses the problem of handling interruptions (barge-in) in voice-based ordering interfaces operating on top of large language models. The aim of the study is, based on a review of solutions for streaming speech recognition, voice activity detection, turn-taking, and end-to-end dialogue architectures, to synthesise an integrated set of barge-in handling strategies for high-load QSR scenarios. The relevance of the research is determined by the growing share of voice orders in noisy environments and their relationship to transaction accuracy, service speed, and customer satisfaction. The novelty of the article lies in treating interruption as an end-to-end intention marker that imposes coherent requirements on stack architecture, dialogue policy, utterance design, and the transactional logic of the cart. It is demonstrated that effective barge-in handling relies on prioritising user speech over system synthesis, streaming recognition with interim hypotheses, separating stable dialogue state from the instantaneous response plan inside the language model, and phase-dependent policies for interpreting identical acoustic patterns at the stages of greeting, order configuration, confirmation, and payment. It is argued that short, interruptible system utterances, explicit invitations to interrupt, and conservative interpretation of interventions at payment steps transform interruption from a source of errors into a controlled mechanism for order correction and failure mitigation. The article is intended for researchers in dialogue systems, voice interface engineers, and product teams developing voice solutions for large-scale ordering scenarios.