@inproceedings{hamanaka-etal-2026-sensor,
title = "Sensor-Augmented Voice Activity Projection for Enhancing Turn-Taking Prediction",
author = "Hamanaka, Satoki and
Kishino, Yasue and
Tsunomori, Yuiko and
Mizutani, Shin and
Chiba, Yuya and
Okoshi, Tadashi and
Nakazawa, Jin",
editor = "Choi, Jinho D. and
Chen, Yun-Nung and
Funakoshi, Kotaro and
Emami, Ali",
booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2026",
address = "Atlanta, Georgia, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.sigdial-1.12/",
pages = "164--169",
abstract = "Voice Activity Projection (VAP) has been actively studied to enable natural turn-taking in spoken dialogue systems, relying primarily on acoustic features. Visual cues such as head movements are also known to contribute to turn-taking prediction; however, camera-based approaches are affected by placement and lighting conditions and are not always reliably available to dialogue systems. As a camera-independent approach for directly capturing head motion, earable devices offer a promising solution. In this study, we propose Sensor-Augmented VAP, a framework that integrates in-ear inertial measurement unit (IMU) signals with a pre-trained VAP model via a lightweight residual fusion module. To validate our proposed method, we collected a dataset pairing conversational audio with in-ear IMU data, comprising 12 dyadic Japanese dialogues recorded using microphones and earbuds. Experiments in speaker-independent and speaker-dependent settings demonstrate that IMU fusion consistently improves weighted F1 score for shift detection and reduces VAP loss over the audio-only baseline. These results confirm that head-motion cues are effective for enhancing turn-taking prediction."
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<abstract>Voice Activity Projection (VAP) has been actively studied to enable natural turn-taking in spoken dialogue systems, relying primarily on acoustic features. Visual cues such as head movements are also known to contribute to turn-taking prediction; however, camera-based approaches are affected by placement and lighting conditions and are not always reliably available to dialogue systems. As a camera-independent approach for directly capturing head motion, earable devices offer a promising solution. In this study, we propose Sensor-Augmented VAP, a framework that integrates in-ear inertial measurement unit (IMU) signals with a pre-trained VAP model via a lightweight residual fusion module. To validate our proposed method, we collected a dataset pairing conversational audio with in-ear IMU data, comprising 12 dyadic Japanese dialogues recorded using microphones and earbuds. Experiments in speaker-independent and speaker-dependent settings demonstrate that IMU fusion consistently improves weighted F1 score for shift detection and reduces VAP loss over the audio-only baseline. These results confirm that head-motion cues are effective for enhancing turn-taking prediction.</abstract>
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%0 Conference Proceedings
%T Sensor-Augmented Voice Activity Projection for Enhancing Turn-Taking Prediction
%A Hamanaka, Satoki
%A Kishino, Yasue
%A Tsunomori, Yuiko
%A Mizutani, Shin
%A Chiba, Yuya
%A Okoshi, Tadashi
%A Nakazawa, Jin
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F hamanaka-etal-2026-sensor
%X Voice Activity Projection (VAP) has been actively studied to enable natural turn-taking in spoken dialogue systems, relying primarily on acoustic features. Visual cues such as head movements are also known to contribute to turn-taking prediction; however, camera-based approaches are affected by placement and lighting conditions and are not always reliably available to dialogue systems. As a camera-independent approach for directly capturing head motion, earable devices offer a promising solution. In this study, we propose Sensor-Augmented VAP, a framework that integrates in-ear inertial measurement unit (IMU) signals with a pre-trained VAP model via a lightweight residual fusion module. To validate our proposed method, we collected a dataset pairing conversational audio with in-ear IMU data, comprising 12 dyadic Japanese dialogues recorded using microphones and earbuds. Experiments in speaker-independent and speaker-dependent settings demonstrate that IMU fusion consistently improves weighted F1 score for shift detection and reduces VAP loss over the audio-only baseline. These results confirm that head-motion cues are effective for enhancing turn-taking prediction.
%U https://aclanthology.org/2026.sigdial-1.12/
%P 164-169
Markdown (Informal)
[Sensor-Augmented Voice Activity Projection for Enhancing Turn-Taking Prediction](https://aclanthology.org/2026.sigdial-1.12/) (Hamanaka et al., SIGDIAL 2026)
ACL
- Satoki Hamanaka, Yasue Kishino, Yuiko Tsunomori, Shin Mizutani, Yuya Chiba, Tadashi Okoshi, and Jin Nakazawa. 2026. Sensor-Augmented Voice Activity Projection for Enhancing Turn-Taking Prediction. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 164–169, Atlanta, Georgia, USA. Association for Computational Linguistics.