Predicting when to laugh with structured ...
Document type :
Communication dans un congrès avec actes
Title :
Predicting when to laugh with structured classification
Author(s) :
Piot, Bilal [Auteur]
Georgia Tech Lorraine [Metz]
Sequential Learning [SEQUEL]
Pietquin, Olivier [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Institut universitaire de France [IUF]
Geist, Matthieu [Auteur]
Georgia Tech Lorraine [Metz]

Georgia Tech Lorraine [Metz]
Sequential Learning [SEQUEL]
Pietquin, Olivier [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Institut universitaire de France [IUF]
Geist, Matthieu [Auteur]
Georgia Tech Lorraine [Metz]
Conference title :
InterSpeech 2014
City :
Singapore
Country :
Singapour
Start date of the conference :
2014-09-14
Book title :
Proceedings of the Annual Conference of the International Speech Communication Association
Publication date :
2014-09
English keyword(s) :
Imitation Learning
Laughter
Structured Classification
Laughter
Structured Classification
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
Today, Embodied Conversational Agents (ECAs) are emerging as natural media to interact with machines. Applications are numerous and ECAs can reduce the technological gap between people by providing user-friendly interfaces. ...
Show more >Today, Embodied Conversational Agents (ECAs) are emerging as natural media to interact with machines. Applications are numerous and ECAs can reduce the technological gap between people by providing user-friendly interfaces. Yet, ECAs are still unable to produce social signals appropriately during their interaction with humans, which tends to make the interaction less instinctive. Especially, very little attention has been paid to the use of laughter in human-avatar interactions despite the crucial role played by laughter in human-human interaction. In this paper, a method for predicting the most appropriate moment for laughing for an ECA is proposed. Imitation learning via a structured classification algorithm is used in this purpose and is shown to produce a behavior similar to humans’ on a practical application: the yes/no game.Show less >
Show more >Today, Embodied Conversational Agents (ECAs) are emerging as natural media to interact with machines. Applications are numerous and ECAs can reduce the technological gap between people by providing user-friendly interfaces. Yet, ECAs are still unable to produce social signals appropriately during their interaction with humans, which tends to make the interaction less instinctive. Especially, very little attention has been paid to the use of laughter in human-avatar interactions despite the crucial role played by laughter in human-human interaction. In this paper, a method for predicting the most appropriate moment for laughing for an ECA is proposed. Imitation learning via a structured classification algorithm is used in this purpose and is shown to produce a behavior similar to humans’ on a practical application: the yes/no game.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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