Assessing user bias in affect detection within context-based spoken dialog systems

Lebai Lutfi, Syaheerah Binti; Fernández Martínez, Fernando; Casanova García, Andrés; López Lebón, Lorena y Montero Martínez, Juan Manuel (2012). Assessing user bias in affect detection within context-based spoken dialog systems. En: "ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust", 03/09/2012 - 06/09/2012, Amsterdam, The Netherlands.

Descripción

Título: Assessing user bias in affect detection within context-based spoken dialog systems
Autor/es:
  • Lebai Lutfi, Syaheerah Binti
  • Fernández Martínez, Fernando
  • Casanova García, Andrés
  • López Lebón, Lorena
  • Montero Martínez, Juan Manuel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Fechas del Evento: 03/09/2012 - 06/09/2012
Lugar del Evento: Amsterdam, The Netherlands
Título del Libro: ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Fecha: 2012
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (111kB) | Vista Previa

Resumen

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 (reflected 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.

Más información

ID de Registro: 19784
Identificador DC: http://oa.upm.es/19784/
Identificador OAI: oai:oa.upm.es:19784
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6406341
Depositado por: Memoria Investigacion
Depositado el: 17 Sep 2013 16:38
Ultima Modificación: 21 Abr 2016 21:13
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM