Evaluating spoken dialogue models under the interactive pattern recognition framework

The new Interactive Pattern Recognition (IPR) framework has been proposed to deal with human-machine interaction. In this context a new formulation has been recently defined to represent a Spoken Dialogue System as an IPR problem. In this work this formulation is applied to define graphical models that deal with Spoken Dialogue Systems. The definition of both a Dialogue Manager and a User Model are shown and the estimation of the parameters and smoothing techniques are presented in the paper. These models were evaluated in a dialogue generation task on two very different corpora: Dihana corpus consisting of Spanish spoken dialogues acquired with the Wizard of Oz technique and Let’s Go corpus consisting of spoken dialogues in English between real users and the Ravenclaw dialogue manager developed by CMU. The results obtained show that original and simulated dialogues exhibited very similar behaviours, thus demonstrating the learning capacity of the proposed models in both a controlled Wizard of Oz task and a spoken dialogue system that interacts with real users. This formulation can then be considered as a promising framework to deal with Spoken Dialogue Systems.