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,
  • Humanities,
  • 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
Journal number
From page
XX
To page
XX
Month
SIN MES
Ranking
13/30
Participants
  • Autor: Syaheerah Binti Lebai Lutfi UPM

Research Group, Departaments and Institutes related
  • Creador: Departamento: Ingeniería Electrónica