Modelling the effects of constraint upon speech-based human–computer interaction

Abstract

Commercial speech systems, for use by the public, rely heavily on prompts which aim to constrain user input to a highly limited vocabulary set. Constraints help to increase the recognition accuracy of the automatic speech recognition device and thus improve dialogue efficiency. However, this strategy can also lengthen interactions because longer prompts are needed to effectively constrain user utterances and more steps are usually needed to complete a task. The current paper argues that to achieve optimal dialogue design solutions it is necessary to balance these conflicting effects of constraint. Two modelling studies are presented in which dialogue constraint levels for a home-banking application were systematically manipulated in order to investigate the effects on overall transaction time. The results indicate that, even with the assumption that high constraint leads to high recognition accuracy, it is difficult for highly constrained dialogues which entail the need for extra dialogue steps (e.g. those using menus) to compete with less-constrained dialogues which do not (e.g. those using queries). The implications of these findings to system design are discussed and it is suggested that the modelling method presented here can provide a useful tool early in the design process.