Authors:
Anastasiia Spirina
;
Maxim Sidorov
;
Roman Sergienko
and
Alexander Schmitt
Affiliation:
Ulm University, Germany
Keyword(s):
Human-Human Interaction, Call Centres, Classification Algorithms, Performances.
Related
Ontology
Subjects/Areas/Topics:
Human-Machine Interfaces
;
Human-Robots Interfaces
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
Abstract:
This work presents the first experimental results on Interaction Quality modelling for human-human conversation, as an adaptation of the Interaction Quality metric for human-computer spoken interaction. The prediction of an Interaction Quality score can be formulated as a classification problem. In this paper we describe the results of applying several classification algorithms such as: Kernel Naïve Bayes Classifier, k-Nearest Neighbours algorithm, Logistic Regression, and Support Vector Machines, to a data set. Moreover, we compare the results of modelling for two approaches for Interaction Quality labelling and consider the results depending on different emotion sets. The results of Interaction Quality modelling for human-human conversation may be used both for improving the service quality in call centres and for improving Spoken Dialogue Systems in terms of flexibility, user-friendliness and human-likeness.
