人机交互平台中情感分类的深度学习模型

Jose Balbuena, Cesar Beltran
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引用次数: 2

摘要

人机交互(HRI)的主要目的是改善机器人与人之间的沟通,特别是服务机器人,其主要功能是与用户进行交互。服务机器人可以是虚拟的,也可以是实体的,比如聊天机器人或人形机器人。随着互联网接入和在线服务使用的增加,聊天机器人的使用呈指数级增长。这种情况促使人们花更多的时间使用这项技术,并试图使其人性化。因此,赋予机器人情感能力已成为该领域的一个重要问题。出于这个原因,本文的目的是分析和比较常见的深度学习技术(CNN, RNN)的性能,这些技术可以用作HRI平台(如聊天机器人或人形机器人)的情感分类器。评估了两种输入信号:文本和人脸图像。此外,还选择了不同的指标来评估模型的准确性和时间性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Models for Emotion Classification in Human Robot Interaction Platforms
Human Robot Interaction (HRI) main purpose is to improve the communication between robots and people, in special the service robots which principal function is interacting with users. Service robots could be virtual or physical, such as a chatbot or humanoid robot. The increase of internet access and the use of online services have produced an exponentially use of chatbots. This situation generate people spending more time using this technology and trying to humanize it. Therefore, giving robots emotional capabilities have become an important issue in the field. For this reason, the purpose of this article is to analyzed and compared the performance of common deep learning techniques (CNN, RNN) that could be used as a emotion classifier for HRI platforms such a chatbots or humanoid robots. Two kind of input signals were evaluated: text and images of faces. In addition, different metrics were selected to evaluate the accuracy and time performance of the models.
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