A multimodal emotion detection system during human-robot interaction.
A multimodal emotion detection system during human-robot interaction.
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DOI：
10.3390/s131115549
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发表时间：
2013-11-14
期刊：
影响因子：
--
通讯作者：
Salichs MA
中科院分区：
文献类型：
--
作者：
Alonso-Martín F;Malfaz M;Sequeira J;Gorostiza JF;Salichs MA
本文提出了一种多模态的社交机器人用户情感检测系统。该系统旨在用于人机交互，并集成为机器人整体交互系统的一部分：机器人对话系统（RDS）。两种模式用于检测情绪：语音和面部表情分析。为了分析用户的声音，开发了一个新的组件：性别和情感语音分析（GEVA），它是使用查克语言编写的。对于面部表情中的情感检测，还开发了性别和情感面部分析（GEFA）系统。最后一个系统集成了两个第三方解决方案：复杂的高速对象识别引擎（海岸）和计算机表情识别引擎（CERT）。一旦这些新组件（GEVA和GEFA）给出了它们的结果，就应用决策规则以便将它们两者给出的信息联合收割机组合起来。这一规则的结果，即检测到的情感，通过交际行为被整合到对话系统中。因此，每个通信行为将检测到的用户情绪等提供给RDS，因此它可以调整其策略，以便在人机对话期间获得更大的满意度。每个新组件，GEVA和GEFA，也可以单独使用。此外，它们还与机器人控制平台ROS（机器人操作系统）集成。与真实的用户进行了几次实验，以确定每个组件的准确性，并设置最终的决策规则。在这些实验中应用该决策规则所获得的结果显示出在自动用户情感识别中的高成功率，改善了由两个信息通道（音频和视觉）分别给出的结果。
In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately.
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影响因子：
4.2
作者：
Baenziger, Tanja;Grandjean, Didier;Scherer, Klaus R.
通讯作者：
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DOI：
10.1037/0096-1523.4.3.373
发表时间：
1978-01-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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DOI：
10.1098/rstb.1983.0065
发表时间：
1983-01-01
影响因子：
6.3
作者：
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通讯作者：
BRUCE, V
DOI：
10.1109/tsmca.2009.2014645
发表时间：
2009-07-01
影响因子：
--
作者：
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通讯作者：
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