Less can be More: What Aspects of Speech Drive End-of-Turn Detection
In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features ...
| Authors | Rini Sharon, Manickavela A, Kadri Hacioglu et al. |
|---|---|
| Primary category | eess.AS |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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