Speculative End-Turn Detector for Efficient Speech Chatbot Assistant

ArXi:2503.23439v2 Announce Type: replace-cross Spoken dialogue systems powered by large language models have nstrated remarkable abilities in understanding human speech and generating appropriate spoken responses. However, these systems struggle with end-turn detection (ETD) -- the ability to distinguish between user turn completion and hesitation. This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations. In this paper, we