Proposal of a Model to Determine the Attention Target for an Agent in Group Discussion with Non-verbal Features.
Proposal of a Model to Determine the Attention Target for an Agent in Group Discussion with Non-verbal Features.
复制标题
提出一种在具有非语言特征的小组讨论中确定智能体注意目标的模型。
DOI：
10.1145/3125739.3125775
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Kazuhiro Kuwabara
中科院分区：
文献类型：
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作者：
Seiya Kimura;Hung-Hsuan Huang;Qi Zhang;Shogo Okada;Naoki Ohta;Kazuhiro Kuwabara
近年来，公司都在寻找他们的雇主沟通技巧。越来越多的公司在招聘中采用小组讨论的方式来评估应聘者的沟通能力。然而，由于缺乏合作伙伴，小组讨论中提高沟通技巧的机会有限。为了解决这个问题，我们正在进行的项目是建立一个虚拟的代理或机器人，可以参与小组讨论，使其用户可以反复练习小组讨论.本文提出了三种情况下，当代理的注意力转向其他参与者的模型：当代理正在发言，当代理正在倾听，当没有participant发言.首先，我们收集了10个四人小组讨论的数据语料库。然后，我们使用低级别的非语言功能，包括其他参与者的注意力，语音韵律，头部运动，和语音转向提取的10小时语料库来训练支持向量机模型，以确定代理的注意力对其他参与者，或材料。检测模型在F-测量范围内的性能在0.4和0.6之间。
In recent years, companies are seeking for communication skill from their employers. More and more companies adopt group discussions in employer recruitment to evaluate the ap- plicants' communication skill. However, the opportunity to improve communication skill in group discussion is limited due to the lack of partners. In order to solve this issue, our ongoing project is aiming to build a virtual agent or a robot that can participate group discussion, so that its users can re- peatedly practice group discussion with it. In this paper, we propose the models in directing the agent's attention toward the other participants in three situations:when the agent is speaking, when the agent is listening, and when no partic- ipant is speaking. First, we gathered a data corpus of the discussion of 10 four-people groups. We then use low-level non-verbal features including attention of other participant, voice prosody, head movements, and speech turn extracted in the 10-hour corpus to train support vector machine models to determine the agent's attention on the other participants, or the material. The performance of the detection models in F-measure range between 0.4 and 0.6.