Christoph Heike, Greetmate Inc, United States

Voice AI applications, such as customer service chatbots, automated phone systems, and virtual receptionists, face a fundamental challenge known as turn detection, which means accurately identifying when a user has finished speaking and when the assistant should respond. Traditional systems typically rely on Voice Activity Detection (VAD), which interprets short pauses as signals of turn completion. However, this approach often fails in natural conversation, where users hesitate, think aloud, or provide structured information over multiple fragments. In this paper, we introduce an advanced turn detection mechanism that leverages semantic understanding, conversational context, and linguistic cues to significantly improve interaction accuracy and naturalness. Our method is based on a novel parallel prompting architecture, in which a dedicated turn detection language model runs in parallel with the assistant’s response generation process. By evaluating speech content in real time, the system can distinguish between genuine end-of-turns and mid-utterance pauses without increasing response latency. Qualitative evaluation demonstrates that our method substantially reduces false interruptions, enhances dialogue fluidity, and advances the goal of human-centered, meaning-aware conversational AI.

Voice AI, Artificial Intelligence, Turn Detection, Parallel Prompting, Human-AI Interaction, Voice Activity Detection, Conversational AI, AI Agents, Agentic AI