Memorias de investigación
Conferencias:
Assessing user bias in affect detection within context-based Spoken Dialog Systems
Año:2012
Áreas de investigación
  • Inteligencia artificial,
  • Percepción del habla,
  • Competencia social, afectiva y emocional
Datos
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
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tecnología del Habla
  • Departamento: Ingeniería Electrónica