Download Affective Computing and Intelligent Interaction: Fourth by Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, PDF

By Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, Shrikanth S. Narayanan (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)

The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed lawsuits of the Fourth overseas convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set offered including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on attractiveness and synthesis of human impact, affect-sensitive functions, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, correct insights from psychology, affective databases, evaluate and annotation tools.

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Read or Download Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II PDF

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Additional info for Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II

Example text

The number of matches played by a player ranged from 2 to 12. 5, respectively, when failures to correctly guess the emotion were averaged in as 20 questions. , “confused” and “confusion”; “frustrated” and “frustration”). The emotions that were played are: admire, adore + , anger, awe + , boredom, bravery ∗ , calm, confidence ∗ , confusion + , contempt, disgust, enthusiasm + , frustration, gratefulness, jealousy, love, proud, relief, serenity, shame, silly, surprised, and thankful. The starred emotion words were disputed by players after the games and those marked by a plus were emotions that were not successfully guessed.

For example, “Amy looks happy girl” must be “Amy looks a happy girl”, or, for the sentence “She is nice person”, is better to have “She is a nice person“. As we can see, these sentence will be more fluent, if the determiner “a” is added. Our solutions to this are: 1) we can use a language model which can fix this deficiency, 2) we can add an extra module in our system so that the user is able to add the determiner in the generated sentence. 3 Conclusion and Future Work This paper presented a model for sentence generation to express emotions.

Fig. 6. 2 Comparison of an on-screen Artifact with a Robot To investigate how the interpretations of the ASEs from an on-screen artifact were different with the ones from a robot, we compared the results from this experiment with the ones acquired in our former study [10]. 43). Since the participants in this paper did not participate in the former study and the utilized speech sounds and the ASEs were the completely same with ones utilized in the former study, these rejection rates acquired in this experiment and those in the former one were then analyzed using a 2 (independent variable in between-subjects factor: types of artifacts, on-screen artifact or robot) x 2 (independent variable in within-subjects factor: type of ASEs, flat or decreasing) mixed ANOVA (dependent variable: rejection rate).

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