Multimodal Contextual Dialogue Breakdown Detection for Conversational AI Models

arXiv:2404.08156v1 Announce Type: new
Abstract: Detecting dialogue breakdown in real time is critical for conversational AI systems, because it enables taking corrective action to successfully complete a task. In spoken dialog systems, this breakdown can be caused by a variety of unexpected situations including high levels of background noise, causing STT mistranscriptions, or unexpected user flows. In particular, industry settings like healthcare, require high precision and high flexibility to navigate differently based on the conversation history and dialogue states. This …

abstract ai models ai systems arxiv breakdown conversational conversational ai cs.cl detection dialog dialogue multimodal noise spoken systems type