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感情認識・HRIarXiv:1605.04072

共感するヒューマンロボットインタラクションに向けて

Towards Empathetic Human-Robot Interactions

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音声や行動から感情・ユーモア・意図を認識し適切に応答する共感モジュールを、信号処理・感情分析・機械学習で実現する仮想アンドロイド「Zara the Supergirl」の試みを報告した論文。

著者: Pascale Fung, Dario Bertero, Yan Wan, Anik Dey, Ricky Ho Yin Chan, Farhad Bin Siddique, Yang Yang, Chien-Sheng Wu, Ruixi Lin

分類: cs.CL, cs.AI, cs.HC, cs.RO

原文アブストラクト

Since the late 1990s when speech companies began providing their customer-service software in the market, people have gotten used to speaking to machines. As people interact more often with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high level communication features such as humor, sarcasm and intention. In order to make such communication possible, the machines need an empathy module in them which can extract emotions from human speech and behavior and can decide the correct response of the robot. Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can "understand" human emotion. We propose Zara the Supergirl as a prototype system of empathetic robots. It is a software based virtual android, with an animated cartoon character to present itself on the screen. She will get "smarter" and more empathetic through its deep learning algorithms, and by gathering more data and learning from it. In this paper, we present our work so far in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition. We hope to explore the future direction of android development and how it can help improve people's lives.

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