# Less can be More: What Aspects of Speech Drive End-of-Turn Detection

arXiv: https://arxiv.org/abs/2609.11066 · HTML: https://mlsanity.com/paper/2609.11066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Voice Activity Projection: Self-supervised Learning of Turn-taking Events](https://mlsanity.com/paper/2205.09812/) (2022)
- [TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog](https://mlsanity.com/paper/2010.10874/) (2020)
- [Modality Dropout for Improved Performance-driven Talking Faces](https://mlsanity.com/paper/2005.13616/) (2020)
- [Dissecting User-Perceived Latency of On-Device E2E Speech Recognition](https://mlsanity.com/paper/2104.02207/) (2021)
- [Turn-Taking Prediction for Natural Conversational Speech](https://mlsanity.com/paper/2208.13321/) (2022)
- [Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems](https://mlsanity.com/paper/2509.23938/) (2025)
- [Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs](https://mlsanity.com/paper/1808.10785/) (2018)
- [Speculative End-Turn Detector for Efficient Speech Chatbot Assistant](https://mlsanity.com/paper/2503.23439/) (2025)
- [Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features](https://mlsanity.com/paper/2310.15261/) (2023)
- [The Role of Prosodic and Lexical Cues in Turn-Taking with Self-Supervised Speech Representations](https://mlsanity.com/paper/2601.13835/) (2026)

## Abstract (arXiv)

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 have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.