Towards human-like spoken dialogue systems

Abstract

This paper presents an overview of methods that can be used to collect and analyse data on user responses to spoken dialogue system components intended to increase human-likeness, and to evaluate how well the components succeed in reaching that goal. Wizard-of-Oz variations, human–human data manipulation, and micro-domains are discussed in this context, as is the use of third-party reviewers to get a measure of the degree of human-likeness. We also present the two-way mimicry target, a model for measuring how well a human–computer dialogue mimics or replicates some aspect of human–human dialogue, including human flaws and inconsistencies. Although we have added a measure of innovation, none of the techniques is new in its entirety. Taken together and described from a human-likeness perspective, however, they form a set of tools that may widen the path towards human-like spoken dialogue systems.