Observatorio de I+D+i UPM

Memorias de investigación
Conferences:
Assessing user bias in affect detection within context-based Spoken Dialog Systems
Year:2012
Research Areas
  • Artificial intelligence,
  • Perception of language,
  • Social, affective and emotional competence
Information
Abstract
This paper presents an empirical evidence of user bias within a laboratory-oriented evaluation of a Spoken Dialog System. Specifically, we addressed user bias in their satisfaction judgements. We question the reliability of this data for modeling user emotion, focusing on contentment and frustration in a spoken dialog system. This bias is detected through machine learning experiments that were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. The target used was the satisfaction rating and the predictors were conversational/dialog features. Our results indicated that standard classifiers were significantly more successful in discriminating frustration and contentment and the intensities of these emotions (re?ected by user satisfaction ratings) from annotator data than from user data. Indirectly, the results showed that conversational features are reliable predictors of the two abovementioned emotions.
International
Si
978-0-7695-4848-7
Entity
ASE/IEEE International Conference on Social Computing, Amsterdam
Entity Nationality
Sin nacionalidad
Place
Amsterdam, The Netherlands
Participants
  • Autor: Syaheerah Binti Lebai Lutfi (UPM)
  • Autor: Fernando Fernandez Martinez (UPM)
  • Autor: Andrés Casanova García (UPM)
  • Autor: Juan Manuel Montero Martinez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Ingeniería Electrónica
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