## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **N** & **T** & **F** \\ \hline
**M** & 0.00015 * & 0.00015 * & 0.00015 * \\
**N** & & 0.33 & 0.82 \\
**T** & & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(p\)-values were obtained in a post-hock Tukey test for individual selection time given to different selection confirmation methods (* \(p\) < 0.05). See the caption of Figure 1 for the description of the confirmation methods. time threshold is associated with the Midas touch problem in its most severe form [4], and long dwell time thresholds and additional confirmatory eye dwell lead to the significantly slower selection, higher cognitive load, and distraction from the main task [3]. Secondly, the eye tracker we used required relatively frequent recalibration, which can distract and annoy the user. Moreover, to reduce the number of recalibrations in the experiment, we used a chin rest, which is not a practical solution in most cases. However, while calibration is a necessary part of conventional eye tracking technologies, approaches exist to enable calibration-free eye tracking [29; 30; 31; 32; 33], and we expect that this problem could be soon solved in commercial applications. In general, the combined gaze-voice technology necessarily inherits certain issues of the eye tracking-based technology, such as issues in eye pupil tracing in some users, but they could be partly or fully solved with the progress in eye tracking technology. Thirdly, we did not test the use of voice confirmation in the presence of various background noises, including speech sounds from people other than the user. This problem could probably be easily solved by using more sophisticated voice detectors, especially such that they could be tuned to the individual features of the user's voice. Finally, in our experiment, we could not model several other factors important for practical use. For example, relatively high scores for the experience with the voice confirmation could be partly a result of novelty, and it is possible that with gaining experience, it could become not only more convenient, but also boring. Thus, the current study should not be considered as the final proof of the feasibility of the approach, and detailed future studies are required to confirm its usefulness for specific applications and user groups. On the other hand, there are ways to further improve the user experience with \"voice-as-click\" confirmation in gaze-based interaction with computers. We used a very simple voice detector, therefore, the participants had to pronounce the words relatively loudly and clearly to avoid misses or delays. The participants quickly understood during practice that voice confirmation should be made relatively loudly, thus, errors related to voice detection were almost absent. However, they likely had to spend certain attentional resources and efforts to maintain that level of loudness. It seems reasonable to expect that more advanced voice/speech detectors (especially for discriminating voice and noise) may enable a significant improvement in user experience and performance. Similarly, a microphone better fit for the task (we used one from a standard headset) also could improve the experience. Furthermore, the license of the eye tracker we in this study used did not grant the right to analyze the gaze data offline; therefore, we could not try to design advanced procedures for the improvement of intention detection based on a joint analysis of gaze and voice features. In future studies, more advanced voice detectors and advanced detection of intention-related patterns in the joint voice and gaze data should be tested. We did not study the selection of static objects with gaze fixations on them, which is the most widely used type of gaze-based interaction. \"Voice-as-click\" enhancement of such selection should also be tested, but we do not see why the results could change significantly in this case.