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首页> 外文会议>International conference on human-computer interaction;International conference on social computing and social media >Estimating Speaker's Engagement from Non-verbal Features Based on an Active Listening Corpus
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Estimating Speaker's Engagement from Non-verbal Features Based on an Active Listening Corpus

机译：基于主动聆听语料库的非言语特征估计说话者的参与度

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摘要

The elderly who live alone are increasing rapidly in these years. For their mental health, maintaining their social life with others is reported useful. Our project aims to develop a listener agent who can engage active listening dialog with the elderly users. Active listening is a communication technique that the listener listens to the speaker carefully and attentively. The listener also ask questions for confirming or showing his/her concern about what the speaker said. For this task, it is essential for the agent to evaluate the user's engagement level (or the attitude) in the conversation. In this paper, we explored an automatic estimation method based on empirical results. An active listening conversation experiment with human-human participants was conducted for corpus collection. The speakers' engagement attitude in the corpus was subjectively evaluated by human evaluators. Support vector regression models dedicated to the periods when the speaker is speaking, the listener is speaking, and no one is speaking are built with non-verbal features extracted from facial expressions, head movements, prosody and speech turns. The resulted accuracy was not high but showed the potential of the proposed method.
机译：那些独自生活的老人在这些年里迅速增加。对于他们的心理健康，据报道，保持他们的社会生活是有用的。我们的项目旨在开发一个可以与老年用户一起参与主动聆听对话的倾听代理商。主动聆听是一种通信技术，听众仔细和仔细地侦听扬声器。听众还提出了确认或表明他/她对演讲者所说的问题的疑问。对于此任务，代理必须评估用户的参与级别（或态度）。在本文中，我们探讨了基于经验结果的自动估计方法。对人类参与者进行了积极的听力谈话实验，用于语料库收集。语料库中的发言者的参与态度由人类评估人员进行主观评估。支持向量的回归模型专用于扬声器正在发言时，侦听器正在发言，并且没有人说话是用面部表情，头部运动，韵律和语音的非语言特征构建的。得到的精度不高，但显示了所提出的方法的潜力。

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