model-based and empirical evaluation of multimodal interactive error correction
Our research addresses the problem of error correction in speech user interfaces. Previous work hypothesized that switching modality could speed up interactive correction of recognition errors (so-called multimodal error correction). We present a user study that compares, on a dictation task, multimodal error correction with conventional interactive correction, such as speaking again, choosing Tom a list, and keyboard input. Results show that multimodal correction is faster than conventional correction without keyboard input, but slower than correction by typing for users with good typing skills. Furthermore, while users initially prefer speech, they learn to avoid ineffective correction modalities with experience. To extrapolate results from this user study we developed a performance model of multimodal interaction that predicts input speed including time needed for error correction. We apply the model to estimate the impact of recognition technology improvements on correction speeds and the influence of recognition accuracy and correction method on the productivity of dictation systems. Our model is a first step towards formalizing multimodal (recognition-based) interaction.
model empirical evaluation multimodality multimodal interaction interaction interactive error correction error correction errors correctness research speech speech user interfaces user user interfaces switch recognition task input skill learning experience performance model timing estimates technologies accuracy method systems quantitive performance model speech and pen input users