Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910973120479232 |
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| author | Heo, Seongsil Murdock, Calvin Proulx, Michael Miller, Christi |
| author_facet | Heo, Seongsil Murdock, Calvin Proulx, Michael Miller, Christi |
| contents | Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localization, structuring it within a spatial constraint to transform it into a reliable predictive cue. Leveraging egocentric behavioral cues, our experiments demonstrate that incorporating gaze data from a single-user significantly improves prediction performance, while gaze data from multiple-users further enhances it by capturing richer conversational dynamics. This study presents a lightweight and privacy-conscious approach to support adaptive, directional sound control, enhancing speech intelligibility in noisy environments, particularly for hearing assistance in smart glasses. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_13688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations Heo, Seongsil Murdock, Calvin Proulx, Michael Miller, Christi Human-Computer Interaction Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localization, structuring it within a spatial constraint to transform it into a reliable predictive cue. Leveraging egocentric behavioral cues, our experiments demonstrate that incorporating gaze data from a single-user significantly improves prediction performance, while gaze data from multiple-users further enhances it by capturing richer conversational dynamics. This study presents a lightweight and privacy-conscious approach to support adaptive, directional sound control, enhancing speech intelligibility in noisy environments, particularly for hearing assistance in smart glasses. |
| title | Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.13688 |