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

Lebai Lutfi, Syaheerah Binti and Fernández Martínez, Fernando and Casanova García, Andrés and López Lebón, Lorena and Montero Martínez, Juan Manuel (2012). Assessing user bias in affect detection within context-based spoken dialog systems. In: "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.

Description

Title: Assessing user bias in affect detection within context-based spoken dialog systems
Author/s:
  • Lebai Lutfi, Syaheerah Binti
  • Fernández Martínez, Fernando
  • Casanova García, Andrés
  • López Lebón, Lorena
  • Montero Martínez, Juan Manuel
Item Type: Presentation at Congress or Conference (Article)
Event Title: ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Event Dates: 03/09/2012 - 06/09/2012
Event Location: Amsterdam, The Netherlands
Title of Book: ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Date: 2012
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 (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.

More information

Item ID: 19784
DC Identifier: http://oa.upm.es/19784/
OAI Identifier: oai:oa.upm.es:19784
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6406341
Deposited by: Memoria Investigacion
Deposited on: 17 Sep 2013 16:38
Last Modified: 21 Apr 2016 21:13
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