# Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task

arXiv: https://arxiv.org/abs/1805.06511 · HTML: https://mlsanity.com/paper/1805.06511/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 6 of 6)

- [Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion](https://mlsanity.com/paper/2401.14717/) (2024)
- [Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue](https://mlsanity.com/paper/2204.10172/) (2022)
- [What BERT Based Language Models Learn in Spoken Transcripts: An Empirical Study](https://mlsanity.com/paper/2109.09105/) (2021)
- [Triadic Multi-party Voice Activity Projection for Turn-taking in Spoken Dialogue Systems](https://mlsanity.com/paper/2507.07518/) (2025)
- [Thai Semantic End-of-Turn Detection for Real-Time Voice Agents](https://mlsanity.com/paper/2510.04016/) (2025)
- [NaturalTurn: A Method to Segment Speech into Psychologically Meaningful Conversational Turns](https://mlsanity.com/paper/2403.15615/) (2024)

## Abstract (arXiv)

This work focuses on the use of acoustic cues for modeling turn-taking in dyadic spoken dialogues. Previous work has shown that speaker intentions (e.g., asking a question, uttering a backchannel, etc.) can influence turn-taking behavior and are good predictors of turn-transitions in spoken dialogues. However, speaker intentions are not readily available for use by automated systems at run-time; making it difficult to use this information to anticipate a turn-transition. To this end, we propose a multi-task neural approach for predicting turn- transitions and speaker intentions simultaneously. Our results show that adding the auxiliary task of speaker intention prediction improves the performance of turn-transition prediction in spoken dialogues, without relying on additional input features during run-time.