arXiv cs.AI· Graham Wilcock, Kristiina Jokinen·· 1 天前AI 评分20
社交机器人话太多听太少：多模态双向流式传输如何减少轮次切换失败
Robots That Talk Too Much and Don't Listen Enough
AI 导读
社交机器人自然语音交互的主要障碍已从语音识别错误转向轮次切换失败等误解，这类问题仍过于普遍。这篇短文回顾了以往改进语音识别与轮次切换的方法，并指出当前需要多模态双向流式传输来降低轮次切换失败频率。
正文 · 原文
Abstract:Until recently, the biggest obstacles to natural spoken interaction with social robots were speech recognition errors and turn-taking failures. Advances in speech recognition have enabled much better spoken interaction, but turn-taking failures and other misunderstandings continue to be far too prevalent. This short paper looks back at earlier methods for improving speech recognition and turn-taking in interaction with social robots, and then looks at what can be done today, arguing that multimodal bidirectional streaming is needed to reduce the frequency of turn-taking failures.
| Comments: | 4 pages, 3 figures, presented at EMF Workshop, HRI 2026, Edinburgh, Scotland |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2611.16473 [cs.RO] |
| (or arXiv:2611.16473v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16473 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Graham Wilcock [view email]
[v1]
Thu, 12 Nov 2026 08:32:18 UTC (6,718 KB)
来源：arXiv cs.AI · arxiv.org