Observatorio de I+D+i UPM

Memorias de investigación
Assessing user bias in affect detection within context-based Spoken Dialog Systems
Research Areas
  • Artificial intelligence,
  • Perception of language,
  • Social, affective and emotional competence
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.
ASE/IEEE International Conference on Social Computing
Entity Nationality
Sin nacionalidad
Amsterdam, The Netherlands
  • 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)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tecnología del Habla
  • Departamento: Ingeniería Electrónica
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)