A Clustering Approach to Assess Real User Profiles in Spoken Dialogue Systems.
Zoraida Callejas, David Griol, Klaus-Peter Engelbrecht, Ramón López-Cózar · IWSDS · 2012
Evaluation methodologies for spoken dialogue systems try to provide an efficient means of assessing the quality of the system and/or predicting the user satisfaction. In order to do so, they must be carried out over a corpus of dialogues which contains as many possible prospective or real user types as possible. In this paper we present a clustering approach to provide insight on whether user profiles can be automatically detected from the interaction parameters and overall quality predictions, providing a way of corroborating the most representative features for defining user profiles. We have carried out different experiments over a corpus of 62 dialogues with the INSPIRE dialogue system, from which the clustering approach provided an efficient way of easily obtaining information about the suitability of distinguishing between different user groups to complete a more significative evaluation of the system.