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Multimodal Interaction History and its use in Error Detection and Recovery

Multimodal systems still tend to ignore the individual input behavior of users, and at the same time, suffer from erroneous sensor inputs. Although many researchers have described user behavior in specific settings and tasks, little to nothing is known about the applicability of such information, when it comes to increase the robustness of a system for multimodal inputs. We conducted a gamified experimental study to investigate individual user behavior and error types found in an actually running system. It is shown, that previous ways of describing input behavior by a simple classification scheme (like simultaneous and sequential) are not suited to build up an individual interaction history. Instead, we propose to use temporal distributions of different metrics derived from multimodal event timings. We identify the major errors that can occur in multimodal interactions and finally show how such an interaction history can practically be applied for error detection and recovery. Applying the proposed approach to the experimental data, the initial error rate is reduced from 4.9% to a minimum of 1.2%.