Cluster-based approach to discriminate the user’s state whether a user is embarrassed or thinking to an answer to a prompt
Cluster-based approach to discriminate the user’s state whether a user is embarrassed or thinking to an answer to a prompt
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基于集群的方法来区分用户的状态，无论用户是感到尴尬还是正在思考对提示的回答
DOI：
10.1007/s12193-017-0238-y
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发表时间：
2017
影响因子：
2.9
通讯作者：
Akinori Ito
中科院分区：
文献类型：
--
作者：
Yuya Chiba;Takashi Nose;Akinori Ito
在各种设备中采用口语对话系统来帮助用户操作它们。口语对话系统的一个优点是用户可以自由地输入话语，但系统有时会使用户很难对它说话。系统应该在开始对话时估计遇到问题的用户的状态，然后在用户放弃对话之前给予适当的帮助。基于这一假设，我们的研究旨在构建一个系统，响应用户谁不回复系统。在本文中，我们提出了一种基于矢量量化的非语言信息，如韵律特征，面部特征点，和目光的用户的状态判别方法。实验结果表明，该方法优于传统的方法，达到72.0%的区分率。然后，我们研究了在适当的时间响应用户的顺序歧视。结果表明，辨别率在6.0 s左右达到等于会话结束。
Spoken dialog systems are employed in various devices to help users operate them. An advantage of a spoken dialog system is that the user can make input utterances freely, but the system sometimes makes it difficult for the user to speak to it. The system should estimate the state of a user who encounters a problem when starting a dialog and then give appropriate help before the user abandons the dialog. Based on this assumption, our research aims to construct a system which responds to a user who does not reply to the system. In this paper, we propose a method of discriminating the user’s state based on vector quantization of non-verbal information such as prosodic features, facial feature points, and gaze. The experimental results showed that the proposed method outperforms the conventional approaches and achieves a discrimination ratio of 72.0%. Then, we examined sequential discrimination for responding to the user at an appropriate timing. The results indicate that the discrimination ratio reached equal to the end of the session at around 6.0 s.