2004年
Recognition of emotional states in spoken dialogue with a robot
INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE
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- 巻
- 3029
- 号
- 開始ページ
- 413
- 終了ページ
- 423
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- 出版者・発行元
- SPRINGER-VERLAG BERLIN
For flexible interactions between a robot and humans, we address the issue of automatic recognition of human emotions during the interaction such as embarrassment, pleasure, and affinity. To construct classifiers of emotions, we used the dialogue data between a humanoid robot, Robovie, and children, which was collected with the WOZ (Wizard of Oz) method. Besides prosodic features extracted from a single utterance, characteristics specific to dialogues such as utterance intervals and differences with previous utterances were also used. We used the SVM (Support Vector Machine) as a classifier to recognize two temporary emotions such as embarrassment or pleasure, and the decision tree learning algorithm, C5.0, as a classifier to recognize persistent emotion, i.e. affinity. The accuracy of classification was 79% for embarrassment, 74% for pleasure, and 87% for affinity. The humanoid Robovie in which this emotion classification module was implemented demonstrated adaptive behaviors based on the emotions it recognized.
- リンク情報
- ID情報
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- ISSN : 0302-9743
- Web of Science ID : WOS:000221714200043