Designing ECAs to Improve Robustness of Human-Machine Dialogue

Abstract

One of the major challenges for dialogue systems deployed in commercial applications is to improve robustness when common low-level problems occur that are related with speech recognition. We first discuss this important family of interaction problems, and then we discuss the features of non-verbal, visual, communication that Embodied Conversational Agents (ECAs) bring ‘into the picture’ and which may be tapped into to improve spoken dialogue robustness and the general smoothness and efficiency of the interaction between the human and the machine. Our approach is centred around the information provided by ECAs. We deal with all stages of the conversation system development process, from scenario description, to gesture design and evaluation with comparative user tests. We conclude that ECAs can help improve the robustness of, as well as the users’ subjective experience with, a dialogue system. However, they may also make users more demanding and intensify privacy and security concerns.