Building Smarter End-Of-Turn Detection for Conversational AI Using Transformer-Based Semantic Models

This paper presents a novel approach to end-of-turn detection for conversational AI systems, combining traditional voice activity detection (VAD) with semantic understanding through a transformer-based model. End-of-turn detection remains one of the most challenging aspects of creating natural conversational AI interfaces, as current systems rely primarily on silence thresholds that fail to capture the semantic cues humans use to determine speaking turns. Our approach leverages a lightweight transformer model based on the Gemma-3-1b architecture that analyzes transcribed speech in real-time to predict when a user has completed their speaking turn. The model utilizes a sliding context window of recent conversation turns to make these predictions. Experimental results demonstrate that our hybrid system reduces unintentional interruptions by 85% compared to VAD-only approaches, with only a 3% rate of false negatives where turns are incorrectly determined to be incomplete. The proposed system operates effectively on consumer hardware with minimal latency (~50ms) and demonstrates robust performance across various conversational contexts including complex information gathering scenarios like address collection and customer service interactions.