# A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment

arXiv: https://arxiv.org/abs/2503.06241 · HTML: https://mlsanity.com/paper/2503.06241/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Unsupervised pretraining transfers well across languages](https://mlsanity.com/paper/2002.02848/) (2020)
- [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)
- [Multilingual Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2403.06487/) (2024)
- [How Much Does Prosody Help Turn-taking? Investigations using Voice Activity Projection Models](https://mlsanity.com/paper/2209.05161/) (2022)
- [Real-time and Continuous Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2401.04868/) (2024)
- [Applying General Turn-taking Models to Conversational Human-Robot Interaction](https://mlsanity.com/paper/2501.08946/) (2025)
- [Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue](https://mlsanity.com/paper/2204.10172/) (2022)

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

- [Prompt-Guided Turn-Taking Prediction](https://mlsanity.com/paper/2506.21191/) (2025)
- [A Framework for Low-Latency, LLM-driven Multimodal Interaction on the Pepper Robot](https://mlsanity.com/paper/2603.21013/) (2026)
- [Toward Signing Activity Projection in Sign Language Interaction](https://mlsanity.com/paper/2606.09424/) (2026)
- [Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders](https://mlsanity.com/paper/2607.07294/) (2026)
- [Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot](https://mlsanity.com/paper/2607.23204/) (2026)
- [Audio-Visual Turn-taking Prediction in Cocktail Party Scenarios](https://mlsanity.com/paper/2609.17056/) (2026)

## Abstract (arXiv)

Turn-taking is a crucial aspect of human-robot interaction, directly influencing conversational fluidity and user engagement. While previous research has explored turn-taking models in controlled environments, their robustness in real-world settings remains underexplored. In this study, we propose a noise-robust voice activity projection (VAP) model, based on a Transformer architecture, to enhance real-time turn-taking in dialogue robots. To evaluate the effectiveness of the proposed system, we conducted a field experiment in a shopping mall, comparing the VAP system with a conventional cloud-based speech recognition system. Our analysis covered both subjective user evaluations and objective behavioral analysis. The results showed that the proposed system significantly reduced response latency, leading to a more natural conversation where both the robot and users responded faster. The subjective evaluations suggested that faster responses contribute to a better interaction experience.