A Hierarchical End-of-Turn Model with Primary Speaker Segmentation for Real-Time Conversational AI

We present a real-time front-end for voice-based conversational AI to enable natural turn-taking in two-speaker scenarios by combining primary speaker segmentation with hierarchical End-of-Turn (EOT) detection. To operate robustly in multi-speaker environments, the system continuously identifies and tracks the primary user, ensuring that downstream EOT decisions are not confounded by background conversations. The tracked activity segments are fed to a hierarchical, causal EOT model that predicts the immediate conversational state by independently analyzing per-speaker speech features from both the primary speaker and the bot. Simultaneously, the model anticipates near-future states (t+10/20/30,ms) through probabilistic predictions that are aware of the conversation partner's speech. Task-specific knowledge distillation compresses wav2vec~2.0 representations (768,D) into a compact MFCC-based student (32,D) for efficient deployment. The system achieves 82% multi-class frame-level F1 and 70.6% F1 on Backchannel detection, with 69.3% F1 on a binary Final vs. Others task. On an end-to-end turn-detection benchmark, our model reaches 87.7% recall vs. 58.9% for Smart Turn~v3 while keeping a median detection latency of 36,ms versus 800--1300,ms. Despite using only 1.14,M parameters, the proposed model matches or exceeds transformer-based baselines while substantially reducing latency and memory footprint, making it suitable for edge deployment.