Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems
Year:2013
Research Areas
  • Artificial intelligence,
  • Perception of language,
  • Electronic technology and of the communications,
  • Electric engineers, electronic and automatic (eil),
  • Social, affective and emotional competence
Information
Abstract
Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.
International
Si
JCR
Si
Title
Speech Communication
ISBN
0167-6393
Impact factor JCR
1,267
Impact info
Datos JCR del año 2011
Volume
55
Journal number
7-8
From page
825
To page
840
Month
SIN MES
Ranking
13/30
Participants
  • Autor: Syaheerah Binti Lebai Lutfi (UPM)
  • Autor: Fernando Fernández Martínez (Universidad Carlos III de Madrid)
  • Autor: Juan Manuel Lucas Cuesta (UPM)
  • Autor: Lorena López Lebón (Empresa Altran)
  • 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)