Observatorio de I+D+i UPM

Descripción
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.
Internacional	Si
ISSN o ISBN	978-0-7695-4848-7
Entidad relacionada	ASE/IEEE International Conference on Social Computing
Nacionalidad Entidad	Sin nacionalidad
Lugar del congreso	Amsterdam, The Netherlands

Esta actividad pertenece a memorias de investigación

• Autor: Syaheerah Binti Lebai Lutfi (UPM)
• Autor: Fernando Fernandez Martinez (UPM)
• Autor: Andrés Casanova García (UPM)
• Autor: Lorena López Lebón (UPM)
• Autor: Juan Manuel Montero Martinez (UPM)

• Creador: Grupo de Investigación: Grupo de Tecnología del Habla
• Departamento: Ingeniería Electrónica